Social Control - Final Models

knitr::opts_chunk$set(echo = TRUE)

rm(list = ls())

#cmdstanr::set_cmdstan_path(path = "C:/Users/kueng/.cmdstan/cmdstan-2.35.0")
#cmdstanr::set_cmdstan_path(path = "C:/Users/pascku/.cmdstan/cmdstan-2.36.0")

library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)
library(see)
library(beepr)
library(DHARMa)
library(digest)
library(tidybayes)
library(ggplot2)



source(file.path('Functions', 'ReportModels.R'))
source(file.path('Functions', 'PrettyTables.R'))
source(file.path('Functions', 'ReportMeasures.R'))
source(file.path('Functions', 'PrepareData.R'))

report_function_hash <- digest::digest(summarize_brms)

system("shutdown /a")

## [1] 1116

# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE
do_priorsense = FALSE
get_bayesfactor = TRUE
check_models = TRUE 

if (get_bayesfactor) {
  stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'BF', 'Rhat', 'ESS')
} else {
  stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'Rhat', 'ESS')
}

options(
  dplyr.print_max = 100, 
  brms.backend = 'cmdstan',
  brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
  brms.file_refit = 'on_change',
  #brms.file_refit = 'always',
  error = function() {
    beepr::beep(sound = 5)
    if (shutdown) {
      system("shutdown /s /t 180")
      quit(save = "no", status = 1)
    }
  }
  , es.use_symbols = TRUE
)


####################### Model parameters #######################

iterations = 12000 # 12'000 per chain to achieve 40'000
warmup = 2000 # 2000

# NO AR!!!
#corstr = 'ar'
#corstr = 'cosy_couple'
#corstr = 'cosy_couple:user'


################################################################

suffix = paste0('_SensitivityBarriersFacilitatorsInteract_', as.character(iterations))

df <- openxlsx::read.xlsx(file.path('long.xlsx'))
df_original <- df

df_double <- prepare_data(df, recode_pushing = TRUE, use_mi = use_mi)[[1]]

Constructing scales Re-coding pusing reshaping data (4field) centering data within and between

summary(df_double$pushing)

Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s 0.0000 0.0000 0.0000 0.1649 0.0000 5.0000 275

Modelling

# For indistinguishable Dyads
model_rows_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'persuasion_self_cw', 
    'persuasion_partner_cw', 
    'pressure_self_cw', 
    'pressure_partner_cw', 
    'pushing_self_cw', 
    'pushing_partner_cw', 
    'day', 
    'plan_selfPlan',
    'plan_partnerPlan', # todo: do we need this??
    'barriers_self_cw', 
    'barriers_partner_cw',
    'facilitators_self_cw', 
    'facilitators_partner_cw',
    'weartime_self_cw',
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'persuasion_self_cb',
    'persuasion_partner_cb',
    'pressure_self_cb',
    'pressure_partner_cb',
    'pushing_self_cb',
    'pushing_partner_cb',
    'barriers_self_cb', 
    'barriers_partner_cb',
    'facilitators_self_cb', 
    'facilitators_partner_cb',
    'weartime_self_cb',
    
    # Interactions
    'persuasion_self_cw:barriers_self_cw', 
    'barriers_self_cw:persuasion_self_cw',
    
    'pressure_self_cw:barriers_self_cw', 
    'barriers_self_cw:pressure_self_cw',
    
    'pushing_self_cw:barriers_self_cw', 
    'barriers_self_cw:pushing_self_cw', 
    
    'persuasion_self_cw:facilitators_self_cw', 
    'facilitators_self_cw:persuasion_self_cw',
    
    'pressure_self_cw:facilitators_self_cw', 
    'facilitators_self_cw:pressure_self_cw', 
    
    'pushing_self_cw:facilitators_self_cw', 
    'facilitators_self_cw:pushing_self_cw', 
    
    'persuasion_partner_cw:barriers_self_cw', 
    'barriers_self_cw:persuasion_partner_cw', 
    
    'pressure_partner_cw:barriers_self_cw', 
    'barriers_self_cw:pressure_partner_cw', 
    
    'pushing_partner_cw:barriers_self_cw', 
    'barriers_self_cw:pushing_partner_cw', 
    
    'persuasion_partner_cw:facilitators_self_cw', 
    'facilitators_self_cw:persuasion_partner_cw', 
    
    'pressure_partner_cw:facilitators_self_cw', 
    'facilitators_self_cw:pressure_partner_cw',
    
    'pushing_partner_cw:facilitators_self_cw', 
    'facilitators_self_cw:pushing_partner_cw', 
    
    'persuasion_self_cw:barriers_partner_cw', 
    'barriers_partner_cw:persuasion_self_cw', 
    
    'pressure_self_cw:barriers_partner_cw',
    'barriers_partner_cw:pressure_self_cw',
    
    'pushing_self_cw:barriers_partner_cw', 
    'barriers_partner_cw:pushing_self_cw', 
    
    'persuasion_self_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:persuasion_self_cw', 
    
    'pressure_self_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:pressure_self_cw', 
    
    'pushing_self_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:pushing_self_cw', 
    
    'persuasion_partner_cw:barriers_partner_cw',
    'barriers_partner_cw:persuasion_partner_cw',
    
    'pressure_partner_cw:barriers_partner_cw', 
    'barriers_partner_cw:pressure_partner_cw',
    
    'pushing_partner_cw:barriers_partner_cw', 
    'barriers_partner_cw:pushing_partner_cw', 
    
    'persuasion_partner_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:persuasion_partner_cw', 
    
    'pressure_partner_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:pressure_partner_cw', 
    
    'pushing_partner_cw:facilitators_partner_cw',
    'facilitators_partner_cw:pushing_partner_cw'
  )


model_rows_fixed_ordinal <- c(
  model_rows_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rows_fixed[2:length(model_rows_fixed)]
)

model_rows_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(persuasion_self_cw)',
  'sd(persuasion_partner_cw)',
  'sd(pressure_self_cw)',
  'sd(pressure_partner_cw)',
  'sd(pushing_self_cw)',
  'sd(pushing_partner_cw)',
  
  'sd(barriers_self_cw)', 
  'sd(barriers_partner_cw)',
  'sd(facilitators_self_cw)',
  'sd(facilitators_partner_cw)',
  
  # '-- CORRELATION STRUCTURE -- ', 
  'sigma'
)

model_rows_random_ordinal <- c(model_rows_random,'disc')

# For indistinguishable Dyads
model_rownames_fixed <- c(
    "Intercept", 
    # "-- WITHIN PERSON MAIN EFFECTS --", 
    "Daily individual's experienced persuasion",  
    "Daily partner's experienced persuasion", 
    "Daily individual's experienced pressure", 
    "Daily partner's experienced pressure", 
    "Daily individual's experienced pushing", 
    "Daily partner's experienced pushing", 
    "Day", 
    "Own action plan",
    'Partner action plan',
    "Daily individuals' barriers",
    "Daily partners' barriers",
    "Daily individuals' facilitators",
    "Daily partners' facilitators",
    "Daily wear time",
    

    # "-- BETWEEN PERSON MAIN EFFECTS",
    "Mean individual's experienced persuasion", 
    "Mean partner's experienced persuasion", 
    "Mean individual's experienced pressure", 
    "Mean partner's experienced pressure", 
    "Mean individual's experienced pushing", 
    "Mean partner's experienced pushing", 
    "Mean individuals' barriers",
    "Mean partners' barriers",
    "Mean individuals' facilitators",
    "Mean partners' facilitators",
    "Mean wear time",
    
    # Interactions
    'persuasion_self_cw:barriers_self_cw', 
    'barriers_self_cw:persuasion_self_cw',
    
    'pressure_self_cw:barriers_self_cw', 
    'barriers_self_cw:pressure_self_cw',
    
    'pushing_self_cw:barriers_self_cw', 
    'barriers_self_cw:pushing_self_cw', 
    
    'persuasion_self_cw:facilitators_self_cw', 
    'facilitators_self_cw:persuasion_self_cw',
    
    'pressure_self_cw:facilitators_self_cw', 
    'facilitators_self_cw:pressure_self_cw', 
    
    'pushing_self_cw:facilitators_self_cw', 
    'facilitators_self_cw:pushing_self_cw', 
    
    'persuasion_partner_cw:barriers_self_cw', 
    'barriers_self_cw:persuasion_partner_cw', 
    
    'pressure_partner_cw:barriers_self_cw', 
    'barriers_self_cw:pressure_partner_cw', 
    
    'pushing_partner_cw:barriers_self_cw', 
    'barriers_self_cw:pushing_partner_cw', 
    
    'persuasion_partner_cw:facilitators_self_cw', 
    'facilitators_self_cw:persuasion_partner_cw', 
    
    'pressure_partner_cw:facilitators_self_cw', 
    'facilitators_self_cw:pressure_partner_cw',
    
    'pushing_partner_cw:facilitators_self_cw', 
    'facilitators_self_cw:pushing_partner_cw', 
    
    'persuasion_self_cw:barriers_partner_cw', 
    'barriers_partner_cw:persuasion_self_cw', 
    
    'pressure_self_cw:barriers_partner_cw',
    'barriers_partner_cw:pressure_self_cw',
    
    'pushing_self_cw:barriers_partner_cw', 
    'barriers_partner_cw:pushing_self_cw', 
    
    'persuasion_self_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:persuasion_self_cw', 
    
    'pressure_self_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:pressure_self_cw', 
    
    'pushing_self_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:pushing_self_cw', 
    
    'persuasion_partner_cw:barriers_partner_cw',
    'barriers_partner_cw:persuasion_partner_cw',
    
    'pressure_partner_cw:barriers_partner_cw', 
    'barriers_partner_cw:pressure_partner_cw',
    
    'pushing_partner_cw:barriers_partner_cw', 
    'barriers_partner_cw:pushing_partner_cw', 
    
    'persuasion_partner_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:persuasion_partner_cw', 
    
    'pressure_partner_cw:facilitators_partner_cw', 
    'facilitators_partner_cw:pressure_partner_cw', 
    
    'pushing_partner_cw:facilitators_partner_cw',
    'facilitators_partner_cw:pushing_partner_cw'
  )


model_rownames_fixed_ordinal <- c(
  model_rownames_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rownames_fixed[2:length(model_rownames_fixed)]
)

model_rownames_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  "sd(Daily individual's experienced persuasion)", 
  "sd(Daily partner's experienced persuasion)", # OR partner received
  "sd(Daily individual's experienced pressure)", 
  "sd(Daily partner's experienced pressure)", 
  "sd(Daily individual's experienced pushing)", 
  "sd(Daily partner's experienced pushing)", 
  
  'sd(barriers_self_cw)', 
  'sd(barriers_partner_cw)', 
  'sd(barriers_partner_cw)',
  'sd(facilitators_self_cw)',
  'sd(facilitators_partner_cw)',
  # '-- CORRELATION STRUCTURE -- ', 
  'sigma'
)

model_rownames_random_ordinal <- c(model_rownames_random,'disc')

rows_to_pack <- list(
  "Within-Person Effects" = c(2,15),
  "Between-Person Effects" = c(16,26),
  "Interactions" = c(27, 74),
  "Random Effects" = c(75, 85), 
  "Additional Parameters" = c(86,86)
  )


rows_to_pack_ordinal <- list(
  "Within-Person Effects" = c(2+5,15+5),
  "Between-Person Effects" = c(16+5,26+5),
  "Interactions" = c(27+5, 74+5),
  "Random Effects" = c(75+5, 85+5), 
  "Additional Parameters" = c(86+5,86+6)
  )

Speficy interactions

interaction_terms <- paste(
    '+ persuasion_self_cw * barriers_self_cw',
    '+ pressure_self_cw * barriers_self_cw',
    '+ pushing_self_cw * barriers_self_cw',
      
    #'+ persuasion_self_cw * facilitators_self_cw',
    #'+ pressure_self_cw * facilitators_self_cw',
    #'+ pushing_self_cw * facilitators_self_cw',
    
    '+ persuasion_partner_cw * barriers_self_cw', 
    '+ pressure_partner_cw * barriers_self_cw', 
    '+ pushing_partner_cw * barriers_self_cw', 
    
    #'+ persuasion_partner_cw * facilitators_self_cw', 
    #'+ pressure_partner_cw * facilitators_self_cw', 
    #'+ pushing_partner_cw * facilitators_self_cw', 
    
    #'+ persuasion_self_cw * barriers_partner_cw', 
    #'+ pressure_self_cw * barriers_partner_cw',
    #'+ pushing_self_cw * barriers_partner_cw', 
    
    #'+ persuasion_self_cw * facilitators_partner_cw', 
    #'+ pressure_self_cw * facilitators_partner_cw', 
    #'+ pushing_self_cw * facilitators_partner_cw', 
    
    #'+ persuasion_partner_cw * barriers_partner_cw', 
    #'+ pressure_partner_cw * barriers_partner_cw', 
    #'+ pushing_partner_cw * barriers_partner_cw', 
  
    #'+ persuasion_partner_cw * facilitators_partner_cw', 
    #'+ pressure_partner_cw * facilitators_partner_cw',
    #'+ pushing_partner_cw * facilitators_partner_cw',
    ""
)


formula_terms <- paste(
  "~ 1",
  "+ persuasion_self_cw + persuasion_partner_cw",
  "+ pressure_self_cw + pressure_partner_cw",
  "+ pushing_self_cw + pushing_partner_cw",
  "+ persuasion_self_cb + persuasion_partner_cb",
  "+ pressure_self_cb + pressure_partner_cb",
  "+ pushing_self_cb + pushing_partner_cb",
  
  "+ barriers_self_cb + facilitators_self_cb",
  #"+ barriers_partner_cb + facilitators_partner_cb",
  "+ day",
  #"+ weartime_self_cw + weartime_self_cb", 
  interaction_terms,
  "+ (1",
  "+ persuasion_self_cw + persuasion_partner_cw",
  "+ pressure_self_cw + pressure_partner_cw",
  "+ pushing_self_cw + pushing_partner_cw",
  interaction_terms, 
  "| dd | coupleID)"
)

Self-Reported MVPA

range(df_double$pa_sub, na.rm = T) 

## [1]   0 720

hist(df_double$pa_sub, breaks = 40) 

hist(log(df_double$pa_sub+00000000001), breaks = 40)

Hurdle Lognormal Model

formula <- bf(
  as.formula(
    paste("pa_sub", formula_terms)
  ),
  
  hu = as.formula(
    formula_terms
  )

  , decomp = 'QR'
  #, autocor = autocor_str
) 

prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 2)", class = "b", dpar = "hu")
  , brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
  , brms::set_prior("normal(0.5, 2.5)", class = "Intercept", dpar = 'hu') # hurdle part
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)

#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = hurdle_lognormal()
#)

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

pa_sub <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::hurdle_lognormal(), 
  #family = brms::hurdle_negbinomial(), 
  #family = brms::hurdle_poisson(),
  #control = list(adapt_delta = 0.90),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 42,
  file = file.path("models_cache_brms", paste0("pa_sub_hu_lognormal", suffix))
  #, file_refit = 'always'
)

## Warning: Rows containing NAs were excluded from the model.

## Start sampling

pa_sub_digest <- digest::digest(pa_sub)

if (check_models) {
  check_brms(pa_sub, log_pp_check = TRUE)
  DHARMa.check_brms.all(pa_sub, integer = TRUE, outliers_type = 'bootstrap')
}

## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF   VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.31 [1.30, 1.32]         1.14      0.76
##  persuasion_partner_cw 1.09 [1.08, 1.10]         1.04      0.92
##       pressure_self_cw 1.08 [1.08, 1.09]         1.04      0.92
##    pressure_partner_cw 1.06 [1.05, 1.07]         1.03      0.94
##        pushing_self_cw 1.06 [1.05, 1.07]         1.03      0.94
##     pushing_partner_cw 1.10 [1.09, 1.11]         1.05      0.91
##     persuasion_self_cb 1.06 [1.05, 1.07]         1.03      0.94
##  persuasion_partner_cb 3.91 [3.86, 3.95]         1.98      0.26
##       pressure_self_cb 3.83 [3.79, 3.88]         1.96      0.26
##    pressure_partner_cb 2.30 [2.28, 2.33]         1.52      0.43
##        pushing_self_cb 2.24 [2.22, 2.27]         1.50      0.45
##     pushing_partner_cb 3.13 [3.09, 3.17]         1.77      0.32
##       barriers_self_cb 3.14 [3.10, 3.17]         1.77      0.32
##   facilitators_self_cb 1.10 [1.09, 1.11]         1.05      0.91
##                    day 1.09 [1.08, 1.10]         1.04      0.92
##       barriers_self_cw 1.27 [1.26, 1.28]         1.13      0.79
##  Tolerance 95% CI
##      [0.76, 0.77]
##      [0.91, 0.92]
##      [0.92, 0.93]
##      [0.93, 0.95]
##      [0.94, 0.95]
##      [0.90, 0.91]
##      [0.94, 0.95]
##      [0.25, 0.26]
##      [0.26, 0.26]
##      [0.43, 0.44]
##      [0.44, 0.45]
##      [0.32, 0.32]
##      [0.32, 0.32]
##      [0.90, 0.91]
##      [0.91, 0.93]
##      [0.78, 0.80]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##        cauchy         91%
##     lognormal          9%
## 
## Predicted Distribution of Response
## 
##                Distribution Probability
##  neg. binomial (zero-infl.)         84%
##               beta-binomial          9%
##                   lognormal          6%
## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 31 observations with a pareto_k > 0.7 in model 'model'.
## We recommend to set 'moment_match = TRUE' in order to perform moment
## matching for problematic observations.

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 5, observations = 1776, p-value =
## 0.4
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.00000000 0.00423705
## sample estimates:
## outlier frequency (expected: 0.00170045045045045 ) 
##                                        0.002815315

if (do_priorsense) {
  priorsense_vars <- c(
      'Intercept',
      'b_persuasion_self_cw',
      'b_persuasion_partner_cw',
      'b_pressure_self_cw',
      'b_pressure_partner_cw',
      'b_pushing_self_cw',
      'b_pushing_partner_cw'
  )
  
  hurdle_priorsense_vars <- c(
    'Intercept_hu',
    'b_hu_persuasion_self_cw',
    'b_hu_persuasion_partner_cw',
    'b_hu_pressure_self_cw',
    'b_hu_pressure_partner_cw',
    'b_hu_pushing_self_cw',
    'b_hu_pushing_partner_cw'
  )
  
  gc()
  priorsense::powerscale_sensitivity(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_dens(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_ecdf(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
  priorsense::powerscale_plot_quantities(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
}

# rope range for continuous part of the model
rope_factor <- sd(log(pa_sub$data$pa_sub[pa_sub$data$pa_sub > 0]))
rope_range_continuous = c(-0.1 * rope_factor, 0.1 * rope_factor)

summary_pa_sub <- summarize_brms(
  pa_sub, 
  stats_to_report = stats_to_report,
  rope_range = rope_range_continuous,
  hu_rope_range = c(-0.18, 0.18),
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) 

## Sampling priors, please wait...

## Warning in summarize_brms(pa_sub, stats_to_report = stats_to_report,
## rope_range = rope_range_continuous, : Coefficients were exponentiated.
## Double check if this was intended.

## Warning in summarize_brms(pa_sub, stats_to_report = stats_to_report,
## rope_range = rope_range_continuous, : Some rows from the model were
## omitted due to your provided model_rows_fixed or model_rows_random
## vectors. Missing rows: sd(persuasion_partner_cw:barriers_self_cw),
## sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw)

## Warning in summarize_brms(pa_sub, stats_to_report = stats_to_report,
## rope_range = rope_range_continuous, : Length of model_rownames does not
## match number of rows

# Print the updated dataframe
summary_pa_sub %>%
  print_df(
    rows_to_pack = rows_to_pack
    )

	exp(Est.)_hu	SE_hu	95% CI_hu	pd_hu	ROPE_hu	inside ROPE_hu	BF_hu	BF_Evidence_hu	Rhat_hu	Bulk_ESS_hu	Tail_ESS_hu	exp(Est.)_nonzero	SE_nonzero	95% CI_nonzero	pd_nonzero	ROPE_nonzero	inside ROPE_nonzero	BF_nonzero	BF_Evidence_nonzero	Rhat_nonzero	Bulk_ESS_nonzero	Tail_ESS_nonzero
Intercept	2.89***	0.83	[ 1.62, 5.14]	1.000	[0.84, 1.20]	0.002	46.729	Very Strong Evidence	1.000	36107	32627	49.15***	4.02	[41.78, 58.06]	1.000	[0.93, 1.08]	0.000	>100	Overwhelming Evidence	1.000	14059	20647
Within-Person Effects
persuasion_self_cw	1.61***	0.19	[ 1.30, 2.07]	1.000	[0.84, 1.20]	0.003	>100	Overwhelming Evidence	1.000	47652	31218	1.04	0.03	[ 0.99, 1.10]	0.928	[0.93, 1.08]	0.904	0.029	Very Strong Evidence for Null	1.000	32436	32732
persuasion_partner_cw	1.44***	0.16	[ 1.17, 1.86]	1.000	[0.84, 1.20]	0.041	18.216	Strong Evidence	1.000	44125	31400	1.02	0.02	[ 0.98, 1.07]	0.811	[0.93, 1.08]	0.988	0.013	Very Strong Evidence for Null	1.000	45651	33711
pressure_self_cw	0.84	0.28	[ 0.43, 1.92]	0.698	[0.84, 1.20]	0.356	0.198	Moderate Evidence for Null	1.000	36472	24492	0.90	0.06	[ 0.78, 1.03]	0.946	[0.93, 1.08]	0.305	0.041	Strong Evidence for Null	1.000	40255	31489
pressure_partner_cw	1.64	0.65	[ 0.85, 5.26]	0.923	[0.84, 1.20]	0.171	0.443	Weak Evidence for Null	1.000	27246	24271	0.96	0.05	[ 0.86, 1.06]	0.791	[0.93, 1.08]	0.756	0.012	Very Strong Evidence for Null	1.000	44440	34295
pushing_self_cw	1.22	0.23	[ 0.82, 1.84]	0.854	[0.84, 1.20]	0.428	0.176	Moderate Evidence for Null	1.000	39647	30278	0.98	0.03	[ 0.92, 1.05]	0.701	[0.93, 1.08]	0.950	0.011	Very Strong Evidence for Null	1.000	39607	37174
pushing_partner_cw	1.60*	0.36	[ 1.07, 2.78]	0.989	[0.84, 1.20]	0.077	1.609	Weak Evidence	1.000	34873	26963	0.96	0.03	[ 0.90, 1.02]	0.907	[0.93, 1.08]	0.854	0.021	Very Strong Evidence for Null	1.000	41168	36861
day	0.88	0.22	[ 0.54, 1.42]	0.702	[0.84, 1.20]	0.480	0.140	Moderate Evidence for Null	1.000	83049	31083	1.03	0.07	[ 0.89, 1.18]	0.647	[0.93, 1.08]	0.684	0.009	Very Strong Evidence for Null	1.000	73504	30624
plan_selfPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
plan_partnerPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_self_cw	0.68	0.17	[ 0.42, 1.12]	0.938	[0.84, 1.20]	0.185	0.432	Weak Evidence for Null	1.000	37413	32446	1.08	0.06	[ 0.97, 1.20]	0.930	[0.93, 1.08]	0.493	0.040	Strong Evidence for Null	1.000	36312	30151
barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
persuasion_self_cb	0.91	0.57	[ 0.25, 3.09]	0.563	[0.84, 1.20]	0.226	0.319	Weak Evidence for Null	1.000	25393	28776	1.07	0.18	[ 0.76, 1.50]	0.651	[0.93, 1.08]	0.329	0.017	Very Strong Evidence for Null	1.000	23466	28917
persuasion_partner_cb	1.35	0.84	[ 0.38, 4.59]	0.684	[0.84, 1.20]	0.202	0.356	Weak Evidence for Null	1.000	25207	28535	0.94	0.16	[ 0.67, 1.31]	0.640	[0.93, 1.08]	0.335	0.017	Very Strong Evidence for Null	1.000	21992	29105
pressure_self_cb	0.23	0.20	[ 0.04, 1.24]	0.956	[0.84, 1.20]	0.040	1.874	Weak Evidence	1.000	36118	33182	0.69	0.28	[ 0.31, 1.59]	0.816	[0.93, 1.08]	0.100	0.037	Strong Evidence for Null	1.000	10355	16869
pressure_partner_cb	0.20	0.17	[ 0.03, 1.03]	0.972	[0.84, 1.20]	0.028	2.636	Weak Evidence	1.000	36552	33738	0.51	0.21	[ 0.23, 1.17]	0.946	[0.93, 1.08]	0.039	0.095	Strong Evidence for Null	1.000	10275	17210
pushing_self_cb	1.35	1.21	[ 0.23, 7.96]	0.632	[0.84, 1.20]	0.152	0.483	Weak Evidence for Null	1.000	33489	31707	1.50	0.45	[ 0.83, 2.70]	0.913	[0.93, 1.08]	0.079	0.043	Strong Evidence for Null	1.000	14333	21871
pushing_partner_cb	1.76	1.59	[ 0.30, 10.33]	0.732	[0.84, 1.20]	0.132	0.555	Weak Evidence for Null	1.000	34246	32112	1.65	0.50	[ 0.91, 2.95]	0.950	[0.93, 1.08]	0.052	0.067	Strong Evidence for Null	1.000	13692	23296
barriers_self_cb	0.50**	0.13	[ 0.30, 0.83]	0.997	[0.84, 1.20]	0.023	5.072	Moderate Evidence	1.000	86915	31419	0.92	0.08	[ 0.78, 1.09]	0.827	[0.93, 1.08]	0.452	0.016	Very Strong Evidence for Null	1.000	56891	32994
barriers_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cb	1.20	0.23	[ 0.82, 1.77]	0.831	[0.84, 1.20]	0.458	0.153	Moderate Evidence for Null	1.000	69399	31737	1.14*	0.06	[ 1.03, 1.27]	0.992	[0.93, 1.08]	0.151	0.258	Moderate Evidence for Null	1.000	38644	32619
facilitators_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Interactions
persuasion_self_cw:barriers_self_cw	0.95	0.20	[ 0.62, 1.46]	0.599	[0.84, 1.20]	0.598	0.104	Moderate Evidence for Null	1.000	40097	30713	0.99	0.04	[ 0.92, 1.07]	0.570	[0.93, 1.08]	0.946	0.010	Very Strong Evidence for Null	1.000	46953	33280
barriers_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_self_cw	0.49	0.26	[ 0.15, 1.54]	0.906	[0.84, 1.20]	0.102	0.721	Weak Evidence for Null	1.000	40753	29719	1.02	0.12	[ 0.79, 1.31]	0.557	[0.93, 1.08]	0.469	0.012	Very Strong Evidence for Null	1.000	39661	31246
barriers_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_self_cw	1.94	0.66	[ 0.99, 4.10]	0.973	[0.84, 1.20]	0.067	1.187	Weak Evidence	1.000	36948	31340	0.96	0.05	[ 0.86, 1.07]	0.777	[0.93, 1.08]	0.716	0.014	Very Strong Evidence for Null	1.000	39441	33259
barriers_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_self_cw	0.68*	0.12	[ 0.46, 0.95]	0.989	[0.84, 1.20]	0.110	1.212	Weak Evidence	1.000	42700	32366	1.06	0.04	[ 0.99, 1.13]	0.946	[0.93, 1.08]	0.709	0.033	Strong Evidence for Null	1.000	57612	34728
barriers_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_self_cw	0.82	0.44	[ 0.26, 2.53]	0.647	[0.84, 1.20]	0.245	0.281	Moderate Evidence for Null	1.000	43577	31306	0.90	0.07	[ 0.75, 1.05]	0.916	[0.93, 1.08]	0.323	0.025	Very Strong Evidence for Null	1.000	42764	30340
barriers_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_self_cw	1.71	0.51	[ 0.99, 3.43]	0.974	[0.84, 1.20]	0.097	0.920	Weak Evidence for Null	1.000	35042	30469	1.05	0.05	[ 0.95, 1.18]	0.851	[0.93, 1.08]	0.669	0.016	Very Strong Evidence for Null	1.000	45960	32440
barriers_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	1.44	0.22	[1.08, 1.97]	NA	NA	NA	NA	NA	1.000	27830	30182	0.31	0.05	[0.23, 0.42]	NA	NA	NA	NA	NA	1.000	18714	26238
sd(persuasion_self_cw)	0.17	0.14	[0.01, 0.51]	NA	NA	NA	NA	NA	1.000	19021	21922	0.10	0.03	[0.05, 0.16]	NA	NA	NA	NA	NA	1.000	15886	16147
sd(persuasion_partner_cw)	0.20	0.15	[0.01, 0.54]	NA	NA	NA	NA	NA	1.000	16993	21418	0.05	0.03	[0.01, 0.11]	NA	NA	NA	NA	NA	1.000	14162	13842
sd(pressure_self_cw)	0.54	0.48	[0.03, 2.05]	NA	NA	NA	NA	NA	1.000	17469	24791	0.08	0.07	[0.00, 0.30]	NA	NA	NA	NA	NA	1.000	19070	23257
sd(pressure_partner_cw)	0.53	0.48	[0.03, 2.08]	NA	NA	NA	NA	NA	1.000	18042	23934	0.05	0.05	[0.00, 0.20]	NA	NA	NA	NA	NA	1.000	24798	22570
sd(pushing_self_cw)	0.61	0.30	[0.07, 1.30]	NA	NA	NA	NA	NA	1.000	12779	12795	0.07	0.04	[0.00, 0.16]	NA	NA	NA	NA	NA	1.000	15015	19267
sd(pushing_partner_cw)	0.64	0.34	[0.07, 1.47]	NA	NA	NA	NA	NA	1.000	13111	14791	0.06	0.04	[0.00, 0.15]	NA	NA	NA	NA	NA	1.000	16130	16326
sd(barriers_self_cw)	1.27	0.22	[0.91, 1.79]	NA	NA	NA	NA	NA	1.000	35412	33128	0.20	0.06	[0.08, 0.33]	NA	NA	NA	NA	NA	1.000	13833	12632
sd(barriers_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_self_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Additional Parameters
sigma	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.66	0.01	[0.63, 0.69]	NA	NA	NA	NA	NA	1.000	45048	31583

# Plot continuous part of model

variable <- c(
  '(Intercept)',
  'b_persuasion_self_cw',
  'b_persuasion_partner_cw',
  'b_pressure_self_cw',
  'b_pressure_partner_cw',
  'b_pushing_self_cw',
  'b_pushing_partner_cw'
)


plot(
  bayestestR::p_direction(pa_sub, parameter = variable),
  priors = TRUE
) + theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length
## is not a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple
## of shorter object length

plot(
  bayestestR::rope(
    pa_sub, 
    parameter = variable, 
    range = rope_range_continuous,
    verbose = F,
    ci = 1
  )
) + theme_bw()

# Hurdle part of the model
variable <- c(
  'b_hu_persuasion_self_cw',
  'b_hu_persuasion_partner_cw',
  'b_hu_pressure_self_cw',
  'b_hu_pressure_partner_cw',
  'b_hu_pushing_self_cw',
  'b_hu_pushing_partner_cw'
)

plot(
  bayestestR::p_direction(pa_sub, parameter = variable),
  priors = TRUE
) + theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length
## is not a multiple of shorter object length
## Warning in `==.default`(dens$Parameter, parameter): longer object length
## is not a multiple of shorter object length

# The rope range for the bernoulli part of the model is -0.18, 0.18
plot(
  bayestestR::rope(pa_sub, parameter = variable, range = c(-0.18, 0.18), ci = 1),
  verbose = FALSE
) + theme_bw()

## Possible multicollinearity between b_hu_persuasion_partner_cb and
##   b_hu_persuasion_self_cb (r = 0.78), b_persuasion_partner_cb and
##   b_persuasion_self_cb (r = 0.73), b_pressure_partner_cb and
##   b_pressure_self_cb (r = 0.81). This might lead to inappropriate
##   results. See 'Details' in '?rope'.

Hurdle part of the model on the left, non-zero part towards the right side of the table

conds_eff <- conditional_spaghetti(
  pa_sub, 
  effects = c(
    'persuasion_self_cw',
    'persuasion_partner_cw',
    'pressure_self_cw',
    'pressure_partner_cw',
    'pushing_self_cw',
    'pushing_partner_cw'
  ),
  x_label = c(
    'Received Persuasion',
    'Exerted Persuasion',
    'Received Pressure',
    'Exerted Pressure',
    'Received Plan-Related Pushing',
    'Exerted Plan-Related Pushing'
  ),
  group_var = 'coupleID',
  plot_full_range = TRUE,
  y_limits = c(0, 100),
  y_label = "Same-Day MVPA",
  y_labels = c('Probability of Being Active', 'Minutes of MVPA When Active', 'Overall Expected Minutes of MVPA'),
  , filter_quantiles = .9995
  , font_family = 'Candara'
)

## Scanning ttf files in C:\Windows/Fonts, C:\Users\pascku\AppData\Local/Microsoft/Windows/Fonts ...

## Extracting .afm files from .ttf files...

## C:\Windows\Fonts\Candara.ttf : Candara already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candarab.ttf : Candara-Bold already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candarai.ttf : Candara-Italic already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candaral.ttf : Candara-Light already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candarali.ttf : Candara-LightItalic already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candaraz.ttf : Candara-BoldItalic already registered in fonts database. Skipping.
## Found FontName for 0 fonts.
## Scanning afm files in C:/Users/pascku/AppData/Local/R/cache/R/renv/cache/v5/windows/R-4.4/x86_64-w64-mingw32/extrafontdb/1.0/a861555ddec7451c653b40e713166c6f/extrafontdb/metrics

## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.

print(conds_eff)

$persuasion_self_cw

## Warning: Removed 105 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Warning: Removed 211 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Picking joint bandwidth of 0.00958

$persuasion_partner_cw

## Warning: Removed 77 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Warning: Removed 174 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Picking joint bandwidth of 0.00839

$pressure_self_cw

## Warning: Removed 45 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Warning: Removed 89 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Picking joint bandwidth of 0.0159

$pressure_partner_cw

## Warning: Removed 25 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Picking joint bandwidth of 0.0303

$pushing_self_cw

## Warning: Removed 13 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Warning: Removed 35 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Picking joint bandwidth of 0.0116

$pushing_partner_cw

## Warning: Removed 16 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Warning: Removed 43 rows containing missing values or values outside the scale
## range (`geom_line()`).

## Picking joint bandwidth of 0.0166

Note. This graphic illustrates the relationship between social control and moderate to vigorous physical activity (MVPA) using a Bayesian Hurdle-Lognormal Multilevel Model. The predictor is centered within individuals to examine how deviations from their average social control relate to same-day MVPA. Shaded areas indicate credible intervals, thick lines show fixed effects, and thin lines represent random effects, highlighting variability across couples. The plots display the probability of being active, expected minutes of MVPA when active, and combined predicted MVPA. The bottom density plot visualizes the posterior distributions of slope estimates, transformed to represent multiplicative changes in odds ratios (hurdle component) or expected values. Medians and 95% credible intervals (2.5th and 97.5th percentiles) are shown. Effects are significant, when the 95% credible interval does not overlap 1.

Comparing effect size of pressure and pushing

hypothesis(pa_sub, "pressure_self_cw < pushing_self_cw")

## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper
## 1 (pressure_self_cw... < 0    -0.09      0.08    -0.23     0.04
##   Evid.Ratio Post.Prob Star
## 1       6.62      0.87     
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Device Based MVPA

range(df_double$pa_obj, na.rm = T) 

## [1]   5.75 971.25

hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)

hist(df_double$pa_obj_log, breaks = 50)

Lognormal Model

formula <- bf(
  as.formula(
    paste("pa_obj", formula_terms, "+ weartime_self_cw + weartime_self_cb")
  )
  
  , decomp = 'QR'
  #, autocor = autocor_str
)


prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 50)", class = "Intercept") 
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = lognormal()
#  )

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

pa_obj_log <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = lognormal(),
  control = list(adapt_delta = 0.99),
  iter = iterations + 200,
  warmup = warmup + 200,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("pa_obj_log_gaussian", suffix))
  #, file_refit = 'always'
)

## Warning: Rows containing NAs were excluded from the model.

pa_obj_log_digest <- digest::digest(pa_obj_log)

if (check_models) {
  check_brms(pa_obj_log, log_pp_check = TRUE)
  DHARMa.check_brms.all(pa_obj_log, integer = TRUE, outliers_type = 'bootstrap')
}

## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF   VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.12 [1.09, 1.16]         1.06      0.89
##  persuasion_partner_cw 1.14 [1.11, 1.18]         1.07      0.88
##       pressure_self_cw 1.09 [1.06, 1.13]         1.04      0.92
##    pressure_partner_cw 1.12 [1.09, 1.16]         1.06      0.89
##        pushing_self_cw 1.20 [1.16, 1.24]         1.09      0.83
##     pushing_partner_cw 1.20 [1.17, 1.25]         1.10      0.83
##       pressure_self_cb 3.66 [3.49, 3.85]         1.91      0.27
##    pressure_partner_cb 3.80 [3.62, 4.00]         1.95      0.26
##       barriers_self_cb 1.12 [1.09, 1.16]         1.06      0.89
##   facilitators_self_cb 1.12 [1.09, 1.16]         1.06      0.89
##                    day 1.06 [1.04, 1.10]         1.03      0.94
##       barriers_self_cw 1.05 [1.02, 1.09]         1.02      0.95
##       weartime_self_cw 1.06 [1.04, 1.10]         1.03      0.94
##       weartime_self_cb 1.23 [1.19, 1.28]         1.11      0.81
##  Tolerance 95% CI
##      [0.86, 0.92]
##      [0.85, 0.90]
##      [0.88, 0.94]
##      [0.86, 0.92]
##      [0.80, 0.86]
##      [0.80, 0.86]
##      [0.26, 0.29]
##      [0.25, 0.28]
##      [0.86, 0.92]
##      [0.86, 0.92]
##      [0.91, 0.97]
##      [0.92, 0.98]
##      [0.91, 0.97]
##      [0.78, 0.84]
## 
## Moderate Correlation
## 
##                   Term  VIF   VIF 95% CI Increased SE Tolerance
##     persuasion_self_cb 6.95 [6.59, 7.33]         2.64      0.14
##  persuasion_partner_cb 7.39 [7.01, 7.80]         2.72      0.14
##        pushing_self_cb 5.59 [5.30, 5.89]         2.36      0.18
##     pushing_partner_cb 5.45 [5.18, 5.74]         2.33      0.18
##  Tolerance 95% CI
##      [0.14, 0.15]
##      [0.13, 0.14]
##      [0.17, 0.19]
##      [0.17, 0.19]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##        cauchy         88%
##     lognormal          9%
##       weibull          3%
## 
## Predicted Distribution of Response
## 
##                Distribution Probability
##                   lognormal         72%
##                     tweedie         16%
##  neg. binomial (zero-infl.)          9%
## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 2 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment
## matching for problematic observations.

## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 11, observations = 1594, p-value =
## 0.02
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.000925345 0.006273526
## sample estimates:
## outlier frequency (expected: 0.00310539523212045 ) 
##                                        0.006900878

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(pa_obj_log, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(pa_obj_log, variable = priorsense_vars)
}

# rope range for lognormal model
rope_factor <- sd(log(pa_obj_log$data$pa_obj))
rope_range_log = c(-0.1 * rope_factor, 0.1 * rope_factor)

summary_pa_obj <- summarize_brms(
  pa_obj_log, 
  stats_to_report = stats_to_report,
  rope_range = rope_range_log,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) 

## Sampling priors, please wait...

## Warning in summarize_brms(pa_obj_log, stats_to_report = stats_to_report,
## : Coefficients were exponentiated. Double check if this was intended.

## Warning in summarize_brms(pa_obj_log, stats_to_report = stats_to_report,
## : Some rows from the model were omitted due to your provided
## model_rows_fixed or model_rows_random vectors. Missing rows:
## sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw),
## sd(persuasion_partner_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw)

## Warning in summarize_brms(pa_obj_log, stats_to_report = stats_to_report,
## : Length of model_rownames does not match number of rows

summary_pa_obj %>%
  print_df(
    rows_to_pack = rows_to_pack
    )

	exp(Est.)	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	122.92***	7.50	[108.77, 138.83]	1.000	[0.94, 1.06]	0.000	>100	Overwhelming Evidence	1.002	6870	14120
Within-Person Effects
persuasion_self_cw	1.03	0.02	[ 0.99, 1.06]	0.908	[0.94, 1.06]	0.974	0.018	Very Strong Evidence for Null	1.000	23617	27233
persuasion_partner_cw	1.03	0.02	[ 0.99, 1.07]	0.950	[0.94, 1.06]	0.952	0.028	Very Strong Evidence for Null	1.000	27240	26910
pressure_self_cw	0.95	0.05	[ 0.85, 1.05]	0.848	[0.94, 1.06]	0.563	0.013	Very Strong Evidence for Null	1.000	28839	25172
pressure_partner_cw	0.98	0.04	[ 0.90, 1.06]	0.726	[0.94, 1.06]	0.820	0.008	Very Strong Evidence for Null	1.000	35044	28996
pushing_self_cw	1.03	0.03	[ 0.97, 1.08]	0.822	[0.94, 1.06]	0.902	0.012	Very Strong Evidence for Null	1.000	30297	26694
pushing_partner_cw	0.99	0.02	[ 0.94, 1.04]	0.655	[0.94, 1.06]	0.980	0.007	Very Strong Evidence for Null	1.000	34670	27675
day	0.97	0.05	[ 0.88, 1.07]	0.725	[0.94, 1.06]	0.711	0.007	Very Strong Evidence for Null	1.000	41413	31002
plan_selfPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
plan_partnerPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_self_cw	0.97	0.03	[ 0.91, 1.03]	0.850	[0.94, 1.06]	0.803	0.015	Very Strong Evidence for Null	1.000	24471	27118
barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cw	1.00***	0.00	[ 1.00, 1.00]	1.000	[0.94, 1.06]	1.000	1.696	Weak Evidence	1.000	46240	30780
Between-Person Effects
persuasion_self_cb	0.96	0.16	[ 0.69, 1.33]	0.603	[0.94, 1.06]	0.284	0.016	Very Strong Evidence for Null	1.001	5272	11415
persuasion_partner_cb	0.82	0.14	[ 0.59, 1.15]	0.881	[0.94, 1.06]	0.149	0.031	Strong Evidence for Null	1.001	5079	10623
pressure_self_cb	1.16	0.21	[ 0.81, 1.67]	0.787	[0.94, 1.06]	0.195	0.013	Very Strong Evidence for Null	1.000	8234	16693
pressure_partner_cb	1.06	0.19	[ 0.75, 1.51]	0.638	[0.94, 1.06]	0.262	0.011	Very Strong Evidence for Null	1.000	7053	14687
pushing_self_cb	1.14	0.27	[ 0.70, 1.82]	0.704	[0.94, 1.06]	0.177	0.015	Very Strong Evidence for Null	1.000	8007	14496
pushing_partner_cb	1.29	0.30	[ 0.81, 2.06]	0.863	[0.94, 1.06]	0.116	0.024	Very Strong Evidence for Null	1.000	7463	14575
barriers_self_cb	0.78***	0.04	[ 0.70, 0.87]	1.000	[0.94, 1.06]	0.000	52.846	Very Strong Evidence	1.000	40907	31422
barriers_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cb	0.97	0.04	[ 0.89, 1.05]	0.791	[0.94, 1.06]	0.762	0.013	Very Strong Evidence for Null	1.000	25201	28784
facilitators_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cb	1.00*	0.00	[ 1.00, 1.00]	0.989	[0.94, 1.06]	1.000	0.138	Moderate Evidence for Null	1.000	23810	28177
Interactions
persuasion_self_cw:barriers_self_cw	0.96	0.02	[ 0.92, 1.01]	0.919	[0.94, 1.06]	0.855	0.018	Very Strong Evidence for Null	1.000	34982	28834
barriers_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_self_cw	1.05	0.09	[ 0.89, 1.26]	0.734	[0.94, 1.06]	0.474	0.009	Very Strong Evidence for Null	1.000	24314	22052
barriers_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_self_cw	0.98	0.03	[ 0.91, 1.05]	0.731	[0.94, 1.06]	0.873	0.008	Very Strong Evidence for Null	1.000	33379	29877
barriers_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_self_cw	1.03	0.03	[ 0.98, 1.09]	0.888	[0.94, 1.06]	0.868	0.014	Very Strong Evidence for Null	1.000	33227	27717
barriers_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_self_cw	0.97	0.06	[ 0.85, 1.10]	0.700	[0.94, 1.06]	0.615	0.008	Very Strong Evidence for Null	1.000	34380	27698
barriers_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_self_cw	1.07	0.04	[ 0.99, 1.16]	0.962	[0.94, 1.06]	0.419	0.032	Strong Evidence for Null	1.000	33826	28576
barriers_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	0.33	0.04	[0.25, 0.43]	NA	NA	NA	NA	NA	1.000	9836	18223
sd(persuasion_self_cw)	0.06	0.02	[0.02, 0.10]	NA	NA	NA	NA	NA	1.000	12418	9002
sd(persuasion_partner_cw)	0.05	0.02	[0.01, 0.10]	NA	NA	NA	NA	NA	1.001	9286	8413
sd(pressure_self_cw)	0.07	0.06	[0.00, 0.24]	NA	NA	NA	NA	NA	1.000	10882	15600
sd(pressure_partner_cw)	0.04	0.03	[0.00, 0.13]	NA	NA	NA	NA	NA	1.000	19515	17192
sd(pushing_self_cw)	0.08	0.04	[0.01, 0.15]	NA	NA	NA	NA	NA	1.000	8938	8950
sd(pushing_partner_cw)	0.05	0.03	[0.00, 0.11]	NA	NA	NA	NA	NA	1.000	11007	13575
sd(barriers_self_cw)	0.14	0.03	[0.08, 0.21]	NA	NA	NA	NA	NA	1.000	17799	20265
sd(barriers_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_self_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Additional Parameters
sigma	0.53	0.01	[0.51, 0.55]	NA	NA	NA	NA	NA	1.000	35524	29989

plot(
  bayestestR::p_direction(pa_obj_log),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-3, 3)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length
## is not a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple
## of shorter object length

plot(
  bayestestR::rope(pa_obj_log, range = rope_range_log, ci = 1)
) + theme_bw()

## Possible multicollinearity between b_persuasion_partner_cb and
##   b_persuasion_self_cb (r = 0.85), b_pressure_partner_cb and
##   b_persuasion_partner_cb (r = 0.7), b_pushing_partner_cb and
##   b_pushing_self_cb (r = 0.76). This might lead to inappropriate
##   results. See 'Details' in '?rope'.

# Nothing significant, no plots

Affect

range(df_double$aff, na.rm = T)

## [1] 0 5

hist(df_double$aff, breaks = 15)

Gaussian

formula <- bf(
  as.formula(
    paste("aff", formula_terms)
  )
  
  , decomp = 'QR'
  #, autocor = autocor_str
)


prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b")
  ,brms::set_prior("normal(0, 20)", class = "Intercept", lb=1, ub=6)
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
  , brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)

#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = gaussian()
#  )

#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

mood_gauss <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  control = list(adapt_delta = 0.95),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("mood_gauss", suffix))
  #, file_refit = 'always'
)

## Warning: Rows containing NAs were excluded from the model.

mood_gauss_digest <- digest::digest(mood_gauss)

if (check_models) {
  check_brms(mood_gauss, log_pp_check = FALSE)
  DHARMa.check_brms.all(mood_gauss, integer = FALSE)
}

## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF    VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.17 [1.13,  1.21]         1.08      0.86
##  persuasion_partner_cw 1.14 [1.11,  1.18]         1.07      0.88
##       pressure_self_cw 1.12 [1.09,  1.16]         1.06      0.89
##    pressure_partner_cw 1.07 [1.05,  1.12]         1.04      0.93
##        pushing_self_cw 1.26 [1.22,  1.31]         1.12      0.79
##     pushing_partner_cw 1.17 [1.13,  1.21]         1.08      0.86
##       pressure_self_cb 4.68 [4.44,  4.93]         2.16      0.21
##    pressure_partner_cb 4.59 [4.36,  4.84]         2.14      0.22
##       barriers_self_cb 1.09 [1.07,  1.14]         1.05      0.91
##   facilitators_self_cb 1.08 [1.05,  1.12]         1.04      0.92
##                    day 1.07 [1.04,  1.11]         1.03      0.94
##       barriers_self_cw 1.06 [1.03,  1.10]         1.03      0.95
##  Tolerance 95% CI
##      [0.82, 0.88]
##      [0.84, 0.90]
##      [0.86, 0.92]
##      [0.90, 0.95]
##      [0.76, 0.82]
##      [0.83, 0.88]
##      [0.20, 0.23]
##      [0.21, 0.23]
##      [0.88, 0.94]
##      [0.89, 0.95]
##      [0.90, 0.96]
##      [0.91, 0.97]
## 
## Moderate Correlation
## 
##                   Term  VIF    VIF 95% CI Increased SE Tolerance
##     persuasion_self_cb 9.89 [9.36, 10.45]         3.14      0.10
##  persuasion_partner_cb 9.73 [9.21, 10.29]         3.12      0.10
##        pushing_self_cb 7.20 [6.82,  7.61]         2.68      0.14
##     pushing_partner_cb 7.27 [6.89,  7.68]         2.70      0.14
##  Tolerance 95% CI
##      [0.10, 0.11]
##      [0.10, 0.11]
##      [0.13, 0.15]
##      [0.13, 0.15]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##        normal         44%
##        cauchy         38%
##   exponential          6%
## 
## Predicted Distribution of Response
## 
##   Distribution Probability
##        tweedie         41%
##  beta-binomial         38%
##    half-cauchy          6%
## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 9 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment
## matching for problematic observations.

## 
##  DHARMa outlier test based on exact binomial test with
##  approximate expectations
## 
## data:  model.check
## outliers at both margin(s) = 7, observations = 1776, p-value =
## 0.09756
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
##  0.001586087 0.008103914
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 ) 
##                                          0.003941441

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(mood_gauss, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(mood_gauss, variable = priorsense_vars)
}

summary_mood <- summarize_brms(
  mood_gauss, 
  stats_to_report = stats_to_report,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) 

## Sampling priors, please wait...

## Warning in summarize_brms(mood_gauss, stats_to_report = stats_to_report,
## : Some rows from the model were omitted due to your provided
## model_rows_fixed or model_rows_random vectors. Missing rows:
## sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw),
## sd(persuasion_partner_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw)

## Warning in summarize_brms(mood_gauss, stats_to_report = stats_to_report,
## : Length of model_rownames does not match number of rows

summary_mood %>%
  print_df(
    rows_to_pack = rows_to_pack
    )

	Est.	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	3.80***	0.11	[ 3.59, 4.02]	1.000	[-0.11, 0.11]	0.000	>100	Overwhelming Evidence	1.000	9033	16338
Within-Person Effects
persuasion_self_cw	0.01	0.02	[-0.03, 0.06]	0.694	[-0.11, 0.11]	1.000	0.005	Very Strong Evidence for Null	1.000	58478	33456
persuasion_partner_cw	0.01	0.02	[-0.04, 0.06]	0.649	[-0.11, 0.11]	1.000	0.005	Very Strong Evidence for Null	1.000	58878	31925
pressure_self_cw	-0.02	0.07	[-0.16, 0.11]	0.643	[-0.11, 0.11]	0.870	0.006	Very Strong Evidence for Null	1.000	51732	33773
pressure_partner_cw	-0.04	0.06	[-0.17, 0.08]	0.749	[-0.11, 0.11]	0.862	0.006	Very Strong Evidence for Null	1.000	45660	30515
pushing_self_cw	0.02	0.04	[-0.06, 0.10]	0.696	[-0.11, 0.11]	0.986	0.006	Very Strong Evidence for Null	1.000	43875	35961
pushing_partner_cw	0.08	0.04	[ 0.00, 0.15]	0.973	[-0.11, 0.11]	0.796	0.042	Strong Evidence for Null	1.000	46215	32014
day	0.25**	0.07	[ 0.10, 0.39]	0.999	[-0.11, 0.11]	0.036	0.804	Weak Evidence for Null	1.000	77689	30117
plan_selfPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
plan_partnerPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_self_cw	-0.26***	0.06	[-0.38, -0.12]	1.000	[-0.11, 0.11]	0.015	7.134	Moderate Evidence	1.000	31242	31401
barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
persuasion_self_cb	0.07	0.29	[-0.52, 0.63]	0.591	[-0.11, 0.11]	0.292	0.014	Very Strong Evidence for Null	1.000	9257	15757
persuasion_partner_cb	0.33	0.28	[-0.25, 0.89]	0.872	[-0.11, 0.11]	0.159	0.027	Very Strong Evidence for Null	1.000	9132	15341
pressure_self_cb	-0.02	0.29	[-0.59, 0.56]	0.527	[-0.11, 0.11]	0.295	0.009	Very Strong Evidence for Null	1.000	11775	20704
pressure_partner_cb	-0.27	0.29	[-0.83, 0.31]	0.825	[-0.11, 0.11]	0.192	0.014	Very Strong Evidence for Null	1.000	11380	20343
pushing_self_cb	0.22	0.41	[-0.61, 1.04]	0.704	[-0.11, 0.11]	0.184	0.014	Very Strong Evidence for Null	1.000	11891	19705
pushing_partner_cb	0.01	0.41	[-0.82, 0.82]	0.507	[-0.11, 0.11]	0.213	0.012	Very Strong Evidence for Null	1.000	11933	18765
barriers_self_cb	-0.57***	0.08	[-0.72, -0.41]	1.000	[-0.11, 0.11]	0.000	>100	Overwhelming Evidence	1.000	78957	31536
barriers_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cb	0.32***	0.06	[ 0.21, 0.44]	1.000	[-0.11, 0.11]	0.000	>100	Overwhelming Evidence	1.000	47983	32535
facilitators_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Interactions
persuasion_self_cw:barriers_self_cw	-0.01	0.04	[-0.09, 0.06]	0.600	[-0.11, 0.11]	0.992	0.005	Very Strong Evidence for Null	1.000	51568	32597
barriers_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_self_cw	-0.15	0.17	[-0.51, 0.20]	0.810	[-0.11, 0.11]	0.352	0.012	Very Strong Evidence for Null	1.000	32979	31122
barriers_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_self_cw	0.07	0.05	[-0.03, 0.17]	0.911	[-0.11, 0.11]	0.783	0.012	Very Strong Evidence for Null	1.000	52402	30949
barriers_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_self_cw	0.02	0.04	[-0.05, 0.09]	0.729	[-0.11, 0.11]	0.991	0.005	Very Strong Evidence for Null	1.000	69494	32479
barriers_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_self_cw	-0.06	0.10	[-0.27, 0.13]	0.730	[-0.11, 0.11]	0.663	0.007	Very Strong Evidence for Null	1.000	38996	30433
barriers_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_self_cw	0.09	0.06	[-0.02, 0.22]	0.941	[-0.11, 0.11]	0.634	0.017	Very Strong Evidence for Null	1.000	37609	28398
barriers_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	0.61	0.08	[0.48, 0.79]	NA	NA	NA	NA	NA	1.000	12201	21564
sd(persuasion_self_cw)	0.04	0.03	[0.00, 0.11]	NA	NA	NA	NA	NA	1.000	16132	22554
sd(persuasion_partner_cw)	0.05	0.03	[0.00, 0.12]	NA	NA	NA	NA	NA	1.000	13197	18920
sd(pressure_self_cw)	0.07	0.06	[0.00, 0.26]	NA	NA	NA	NA	NA	1.000	23498	24276
sd(pressure_partner_cw)	0.08	0.07	[0.00, 0.28]	NA	NA	NA	NA	NA	1.000	18844	21409
sd(pushing_self_cw)	0.09	0.05	[0.01, 0.20]	NA	NA	NA	NA	NA	1.000	11728	13943
sd(pushing_partner_cw)	0.09	0.05	[0.01, 0.21]	NA	NA	NA	NA	NA	1.000	14414	16923
sd(barriers_self_cw)	0.33	0.05	[0.24, 0.45]	NA	NA	NA	NA	NA	1.000	30432	32326
sd(barriers_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_self_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Additional Parameters
sigma	0.84	0.02	[0.81, 0.87]	NA	NA	NA	NA	NA	1.000	61376	29276

plot(
  bayestestR::p_direction(mood_gauss),
  priors = TRUE
)  + 
  coord_cartesian(xlim = c(-3, 3)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length
## is not a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple
## of shorter object length

plot(
  bayestestR::rope(mood_gauss, ci = 1)
) + theme_bw()

## Possible multicollinearity between b_pressure_self_cb and
##   b_persuasion_self_cb (r = 0.75), b_pressure_partner_cb and
##   b_persuasion_self_cb (r = 0.72), b_pressure_self_cb and
##   b_persuasion_partner_cb (r = 0.72), b_pressure_partner_cb and
##   b_persuasion_partner_cb (r = 0.75), b_pushing_partner_cb and
##   b_pushing_self_cb (r = 0.84). This might lead to inappropriate
##   results. See 'Details' in '?rope'.

conditional_spaghetti(
  mood_gauss, 
  effects = c('pushing_partner_cw'),
  group_var = 'coupleID',
  plot_full_range = TRUE
)

$pushing_partner_cw

Reactance

range(df_double$reactance, na.rm = T) 

## [1] 0 5

hist(df_double$reactance, breaks = 7) 

hist(log(df_double$reactance+0.1), breaks = 10)

Ordinal

df_double$reactance_ordinal <- factor(df_double$reactance,
                                      levels = 0:5, 
                                      ordered = TRUE)

formula <- bf(
  as.formula(
    paste("reactance_ordinal", formula_terms)
  )
  
  , decomp = 'QR'
  #, autocor = autocor_str
)


prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = cumulative() # HURDLE_CUMULATIVE
#  )


#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

reactance_ordinal <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::cumulative(),
  #control = list(adapt_delta = 0.95),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777
  , file = file.path("models_cache_brms", paste0("reactance_ordinal", suffix))
)

## Warning: Rows containing NAs were excluded from the model.

reactance_ordinal_digest <- digest::digest(reactance_ordinal)

if (check_models) {
  check_brms(reactance_ordinal)
  DHARMa.check_brms.all(reactance_ordinal, outliers_type = 'bootstrap')
}

## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF    VIF 95% CI Increased SE Tolerance
##       pressure_self_cw 4.08 [3.86,  4.32]         2.02      0.25
##    pressure_partner_cw 1.45 [1.39,  1.51]         1.20      0.69
##        pushing_self_cw 1.23 [1.19,  1.29]         1.11      0.81
##     pushing_partner_cw 1.18 [1.14,  1.23]         1.09      0.84
##     persuasion_self_cb 1.07 [1.04,  1.12]         1.04      0.93
##  persuasion_partner_cb 1.10 [1.07,  1.15]         1.05      0.91
##       pressure_self_cb 1.29 [1.24,  1.34]         1.13      0.78
##    pressure_partner_cb 1.28 [1.24,  1.34]         1.13      0.78
##        pushing_self_cb 4.91 [4.63,  5.21]         2.22      0.20
##   facilitators_self_cb 5.00 [4.71,  5.30]         2.24      0.20
##                    day 2.07 [1.97,  2.17]         1.44      0.48
##       barriers_self_cw 2.35 [2.24,  2.48]         1.53      0.42
##  Tolerance 95% CI
##      [0.23, 0.26]
##      [0.66, 0.72]
##      [0.78, 0.84]
##      [0.81, 0.87]
##      [0.89, 0.96]
##      [0.87, 0.94]
##      [0.75, 0.81]
##      [0.75, 0.81]
##      [0.19, 0.22]
##      [0.19, 0.21]
##      [0.46, 0.51]
##      [0.40, 0.45]
## 
## Moderate Correlation
## 
##                   Term  VIF    VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 8.13 [7.65,  8.64]         2.85      0.12
##  persuasion_partner_cw 9.88 [9.29, 10.51]         3.14      0.10
##     pushing_partner_cb 6.22 [5.86,  6.61]         2.49      0.16
##       barriers_self_cb 5.12 [4.83,  5.43]         2.26      0.20
##  Tolerance 95% CI
##      [0.12, 0.13]
##      [0.10, 0.11]
##      [0.15, 0.17]
##      [0.18, 0.21]
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'x' in selecting a method for function 'print': Predictive errors are not defined for ordinal or categorical models.
## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 33 observations with a pareto_k > 0.7 in model 'model'.
## We recommend to set 'moment_match = TRUE' in order to perform moment
## matching for problematic observations.

## 
##  DHARMa bootstrapped outlier test
## 
## data:  model.check
## outliers at both margin(s) = 1, observations = 486, p-value =
## 0.4
## alternative hypothesis: two.sided
##  percent confidence interval:
##  0.000000000 0.002057613
## sample estimates:
## outlier frequency (expected: 0.000411522633744856 ) 
##                                         0.002057613

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(reactance_ordinal, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(reactance_ordinal, variable = priorsense_vars)
}

summary_reactance_ordinal <- summarize_brms(
  reactance_ordinal, 
  stats_to_report = stats_to_report,
  rope_range = c(-0.18, 0.18),
  model_rows_fixed = model_rows_fixed_ordinal,
  model_rows_random = model_rows_random_ordinal,
  model_rownames_fixed = model_rownames_fixed_ordinal,
  model_rownames_random = model_rownames_random_ordinal,
  exponentiate = T) 

## Sampling priors, please wait...

===> warning: knot 3 removed - double knot ===> warning: knot 4 removed - double knot ===> warning: knot 5 removed - double knot * several double knots suggests that your data is strongly rounded, attention might be required possible infinite density at lower end running program with fewer knots ===> warning: knot 3 removed - double knot ===> warning: knot 4 removed - double knot ===> warning: knot 5 removed - double knot several double knots suggests that your data is strongly rounded, attention might be required * possible infinite density at lower end running program with fewer knots

## Warning in summarize_brms(reactance_ordinal, stats_to_report =
## stats_to_report, : Some rows from the model were omitted due to your
## provided model_rows_fixed or model_rows_random vectors. Missing rows:
## sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw),
## sd(persuasion_partner_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw)

## Warning in summarize_brms(reactance_ordinal, stats_to_report =
## stats_to_report, : Length of model_rownames does not match number of
## rows

summary_reactance_ordinal %>%
  print_df(
    rows_to_pack = rows_to_pack_ordinal
    )

	OR	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Intercept[1]	4.62***	1.92	[ 2.06, 10.96]	1.000	[0.84, 1.20]	0.001	30.213	Very Strong Evidence	1.000	49636	35952
Intercept[2]	11.18***	4.82	[ 4.87, 27.84]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	48764	34385
Intercept[3]	35.95***	16.93	[ 14.79, 98.03]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	45655	32137
Intercept[4]	191.28***	107.60	[ 67.26, 619.86]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	43599	32714
Intercept[5]	5962.77***	6286.89	[965.89, 67535.08]	1.000	[0.84, 1.20]	0.000	>100	Overwhelming Evidence	1.000	53557	31040
Within-Person Effects
persuasion_self_cw	0.67**	0.09	[ 0.49, 0.87]	0.998	[0.84, 1.20]	0.050	5.148	Moderate Evidence	1.000	38402	27272
persuasion_partner_cw	0.91	0.15	[ 0.62, 1.24]	0.720	[0.84, 1.20]	0.641	0.086	Strong Evidence for Null	1.000	37579	31837
pressure_self_cw	1.76	0.53	[ 0.88, 3.27]	0.950	[0.84, 1.20]	0.101	0.452	Weak Evidence for Null	1.000	36287	29812
pressure_partner_cw	1.22	0.48	[ 0.46, 2.64]	0.685	[0.84, 1.20]	0.299	0.105	Moderate Evidence for Null	1.000	36023	25749
pushing_self_cw	1.39*	0.21	[ 1.02, 1.89]	0.980	[0.84, 1.20]	0.166	0.634	Weak Evidence for Null	1.000	43760	33120
pushing_partner_cw	0.93	0.18	[ 0.63, 1.38]	0.645	[0.84, 1.20]	0.613	0.075	Strong Evidence for Null	1.000	47812	32684
day	1.35	0.75	[ 0.45, 4.02]	0.708	[0.84, 1.20]	0.218	0.069	Strong Evidence for Null	1.000	59738	33884
plan_selfPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
plan_partnerPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_self_cw	1.00	0.41	[ 0.44, 2.26]	0.502	[0.84, 1.20]	0.343	0.073	Strong Evidence for Null	1.000	46807	34930
barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
persuasion_self_cb	1.46	1.25	[ 0.26, 8.46]	0.671	[0.84, 1.20]	0.149	0.097	Strong Evidence for Null	1.000	32017	31779
persuasion_partner_cb	1.81	1.73	[ 0.29, 13.62]	0.735	[0.84, 1.20]	0.125	0.108	Moderate Evidence for Null	1.000	32530	31009
pressure_self_cb	2.68	2.77	[ 0.35, 22.07]	0.826	[0.84, 1.20]	0.089	0.118	Moderate Evidence for Null	1.000	38087	35401
pressure_partner_cb	1.26	1.24	[ 0.16, 8.36]	0.594	[0.84, 1.20]	0.139	0.075	Strong Evidence for Null	1.000	32420	32304
pushing_self_cb	0.93	1.14	[ 0.08, 11.41]	0.521	[0.84, 1.20]	0.117	0.083	Strong Evidence for Null	1.000	33503	31761
pushing_partner_cb	0.02**	0.03	[ 0.00, 0.41]	0.995	[0.84, 1.20]	0.004	2.465	Weak Evidence	1.000	35224	32193
barriers_self_cb	0.99	0.71	[ 0.23, 4.07]	0.506	[0.84, 1.20]	0.198	0.075	Strong Evidence for Null	1.000	47717	34334
barriers_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cb	0.80	0.35	[ 0.34, 1.95]	0.690	[0.84, 1.20]	0.280	0.107	Moderate Evidence for Null	1.000	43079	32459
facilitators_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Interactions
persuasion_self_cw:barriers_self_cw	0.72	0.15	[ 0.46, 1.09]	0.940	[0.84, 1.20]	0.234	0.289	Moderate Evidence for Null	1.000	46083	33103
barriers_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_self_cw	1.88	0.81	[ 0.74, 4.55]	0.919	[0.84, 1.20]	0.116	0.232	Moderate Evidence for Null	1.000	33935	30324
barriers_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_self_cw	0.78	0.19	[ 0.45, 1.27]	0.849	[0.84, 1.20]	0.347	0.124	Moderate Evidence for Null	1.000	40811	31072
barriers_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_self_cw	1.39	0.42	[ 0.73, 2.67]	0.860	[0.84, 1.20]	0.256	0.171	Moderate Evidence for Null	1.000	43165	30649
barriers_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_self_cw	0.88	0.60	[ 0.22, 3.96]	0.574	[0.84, 1.20]	0.201	0.098	Strong Evidence for Null	1.000	38959	30418
barriers_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_self_cw	1.36	0.48	[ 0.68, 2.77]	0.813	[0.84, 1.20]	0.273	0.118	Moderate Evidence for Null	1.000	51231	33221
barriers_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	1.22	0.32	[0.69, 1.99]	NA	NA	NA	NA	NA	1.000	18872	27617
sd(persuasion_self_cw)	0.33	0.22	[0.02, 0.78]	NA	NA	NA	NA	NA	1.000	10491	18771
sd(persuasion_partner_cw)	0.21	0.17	[0.01, 0.64]	NA	NA	NA	NA	NA	1.000	22296	24694
sd(pressure_self_cw)	0.81	0.42	[0.12, 1.86]	NA	NA	NA	NA	NA	1.000	13988	15164
sd(pressure_partner_cw)	0.56	0.49	[0.02, 2.07]	NA	NA	NA	NA	NA	1.000	17825	24858
sd(pushing_self_cw)	0.31	0.23	[0.02, 0.83]	NA	NA	NA	NA	NA	1.000	12985	21502
sd(pushing_partner_cw)	0.24	0.21	[0.01, 0.87]	NA	NA	NA	NA	NA	1.000	20012	26071
sd(barriers_self_cw)	1.03	0.50	[0.11, 2.07]	NA	NA	NA	NA	NA	1.000	14688	15119
sd(barriers_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_self_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Additional Parameters
sigma	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
disc	1.00	0.00	[1.00, 1.00]	NA	NA	NA	NA	NA	NA	NA	NA

plot(
  bayestestR::p_direction(reactance_ordinal),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-6, 6)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length
## is not a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple
## of shorter object length

plot(
  bayestestR::rope(reactance_ordinal, range = c(-0.18, 0.18), ci = 1)
) + theme_bw()

## Possible multicollinearity between b_Intercept[4] and
##   b_Intercept[2] (r = 0.79), b_Intercept[4] and b_Intercept[3] (r =
##   0.85), b_pressure_partner_cb and b_persuasion_partner_cb (r =
##   0.77). This might lead to inappropriate results. See 'Details' in
##   '?rope'.

#conditional_spaghetti(
#  reactance_ordinal, 
#  effects = c('persuasion_self_cw', 'pressure_self_cw')
#  , group_var = 'coupleID'
#  #, n_groups = 15
#  , plot_full_range = T
#)

Binary

introduce_binary_reactance <- function(data) {
  data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
  return(data)
}



df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
  for (i in seq_along(implist)) {
    implist[[i]] <- introduce_binary_reactance(implist[[i]])
  }
}


formula <- bf(
  as.formula(
    paste("is_reactance", formula_terms)
  )
  
  , decomp = 'QR'
  #, autocor = autocor_str
  )



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b")
  , brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5) 
  , brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)


#brms::validate_prior(
#  prior1, 
#  formula = formula, 
#  data = df_double, 
#  family = bernoulli()
#  )



#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]

is_reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::bernoulli(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = iterations,
  warmup = warmup,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path("models_cache_brms", paste0("is_reactance", suffix))
  #, file_refit = 'always'
)

## Warning: Rows containing NAs were excluded from the model.

is_reactance_digest <- digest::digest(is_reactance)

if (check_models) {
  check_brms(is_reactance)
  DHARMa.check_brms.all(is_reactance, integer = FALSE)
}

## # Check for Multicollinearity
## 
## Low Correlation
## 
##                   Term  VIF   VIF 95% CI Increased SE Tolerance
##     persuasion_self_cw 1.11 [1.08, 1.16]         1.05      0.90
##  persuasion_partner_cw 1.25 [1.20, 1.30]         1.12      0.80
##       pressure_self_cw 1.04 [1.02, 1.10]         1.02      0.96
##    pressure_partner_cw 1.05 [1.03, 1.10]         1.03      0.95
##        pushing_self_cw 1.11 [1.08, 1.16]         1.06      0.90
##     pushing_partner_cw 1.33 [1.28, 1.38]         1.15      0.75
##     persuasion_self_cb 3.66 [3.46, 3.87]         1.91      0.27
##  persuasion_partner_cb 3.82 [3.62, 4.04]         1.95      0.26
##       pressure_self_cb 2.35 [2.24, 2.47]         1.53      0.43
##    pressure_partner_cb 2.27 [2.17, 2.39]         1.51      0.44
##        pushing_self_cb 2.35 [2.24, 2.48]         1.53      0.43
##     pushing_partner_cb 2.30 [2.19, 2.42]         1.52      0.44
##       barriers_self_cb 1.24 [1.19, 1.29]         1.11      0.81
##   facilitators_self_cb 1.08 [1.05, 1.12]         1.04      0.93
##                    day 1.05 [1.03, 1.10]         1.03      0.95
##       barriers_self_cw 1.40 [1.34, 1.46]         1.18      0.72
##  Tolerance 95% CI
##      [0.86, 0.93]
##      [0.77, 0.83]
##      [0.91, 0.98]
##      [0.91, 0.97]
##      [0.86, 0.93]
##      [0.72, 0.78]
##      [0.26, 0.29]
##      [0.25, 0.28]
##      [0.40, 0.45]
##      [0.42, 0.46]
##      [0.40, 0.45]
##      [0.41, 0.46]
##      [0.78, 0.84]
##      [0.89, 0.95]
##      [0.91, 0.97]
##      [0.69, 0.74]
## # Distribution of Model Family
## 
## Predicted Distribution of Residuals
## 
##  Distribution Probability
##        normal         31%
##          beta         19%
##        cauchy         19%
## 
## Predicted Distribution of Response
## 
##   Distribution Probability
##      bernoulli         75%
##  beta-binomial         16%
##       binomial          9%
## 
## Divergences:

## 0 of 40000 iterations ended with a divergence.

## 
## Tree depth:

## 0 of 40000 iterations saturated the maximum tree depth of 10.

## 
## Energy:

## E-BFMI indicated no pathological behavior.

## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

## Using 10 posterior draws for ppc type 'dens_overlay' by default.

## Warning: Found 96 observations with a pareto_k > 0.7 in model 'model'.
## We recommend to set 'moment_match = TRUE' in order to perform moment
## matching for problematic observations.

## 
##  DHARMa outlier test based on exact binomial test with
##  approximate expectations
## 
## data:  model.check
## outliers at both margin(s) = 0, observations = 486, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
##  0.000000000 0.007561553
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 ) 
##                                                    0

if (do_priorsense) {
  gc()
  priorsense::powerscale_sensitivity(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_dens(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_ecdf(is_reactance, variable = priorsense_vars)
  priorsense::powerscale_plot_quantities(is_reactance, variable = priorsense_vars)
}

summary_is_reactance <- summarize_brms(
  is_reactance, 
  stats_to_report = stats_to_report,
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) 

## Sampling priors, please wait...

## Warning in summarize_brms(is_reactance, stats_to_report =
## stats_to_report, : Some rows from the model were omitted due to your
## provided model_rows_fixed or model_rows_random vectors. Missing rows:
## sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw),
## sd(persuasion_partner_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw)

## Warning in summarize_brms(is_reactance, stats_to_report =
## stats_to_report, : Length of model_rownames does not match number of
## rows

summary_is_reactance %>%
  print_df(
    rows_to_pack = rows_to_pack
    )

	OR	SE	95% CI	pd	ROPE	inside ROPE	BF	BF_Evidence	Rhat	Bulk_ESS	Tail_ESS
Intercept	0.47	0.24	[0.16, 1.34]	0.924	[0.83, 1.20]	0.095	0.253	Moderate Evidence for Null	1.000	33627	33018
Within-Person Effects
persuasion_self_cw	0.63*	0.14	[0.38, 0.95]	0.986	[0.83, 1.20]	0.084	1.396	Weak Evidence	1.000	28023	28635
persuasion_partner_cw	1.07	0.32	[0.60, 2.14]	0.596	[0.83, 1.20]	0.453	0.132	Moderate Evidence for Null	1.000	24199	24283
pressure_self_cw	4.29*	3.57	[1.00, 32.57]	0.975	[0.83, 1.20]	0.028	1.590	Weak Evidence	1.000	22611	23694
pressure_partner_cw	1.64	1.47	[0.26, 16.69]	0.716	[0.83, 1.20]	0.142	0.223	Moderate Evidence for Null	1.000	27214	25172
pushing_self_cw	1.70*	0.42	[1.10, 3.13]	0.991	[0.83, 1.20]	0.060	1.632	Weak Evidence	1.000	22475	26993
pushing_partner_cw	0.83	0.30	[0.40, 1.85]	0.699	[0.83, 1.20]	0.335	0.157	Moderate Evidence for Null	1.000	30415	26455
day	1.42	1.01	[0.34, 5.70]	0.690	[0.83, 1.20]	0.178	0.087	Strong Evidence for Null	1.000	49157	32314
plan_selfPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
plan_partnerPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_self_cw	0.82	0.48	[0.25, 2.82]	0.633	[0.83, 1.20]	0.228	0.114	Moderate Evidence for Null	1.000	37379	32322
barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Between-Person Effects
persuasion_self_cb	4.16	5.95	[0.25, 90.18]	0.844	[0.83, 1.20]	0.061	0.246	Moderate Evidence for Null	1.000	18736	24685
persuasion_partner_cb	7.65	11.79	[0.42, 218.29]	0.914	[0.83, 1.20]	0.040	0.344	Weak Evidence for Null	1.000	18245	24902
pressure_self_cb	68.56	167.69	[0.81, 13217.49]	0.969	[0.83, 1.20]	0.012	0.881	Weak Evidence for Null	1.000	23568	27135
pressure_partner_cb	0.65	1.76	[0.00, 111.32]	0.562	[0.83, 1.20]	0.054	0.202	Moderate Evidence for Null	1.000	17143	26302
pushing_self_cb	0.81	1.77	[0.01, 59.15]	0.537	[0.83, 1.20]	0.065	0.150	Moderate Evidence for Null	1.000	20753	27025
pushing_partner_cb	0.01*	0.02	[0.00, 0.70]	0.983	[0.83, 1.20]	0.007	1.449	Weak Evidence	1.000	20611	27523
barriers_self_cb	0.67	0.75	[0.07, 5.80]	0.638	[0.83, 1.20]	0.120	0.123	Moderate Evidence for Null	1.000	32817	31595
barriers_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cb	0.99	0.66	[0.26, 3.96]	0.508	[0.83, 1.20]	0.212	0.144	Moderate Evidence for Null	1.000	26899	29198
facilitators_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Interactions
persuasion_self_cw:barriers_self_cw	0.93	0.26	[0.51, 1.65]	0.601	[0.83, 1.20]	0.462	0.120	Moderate Evidence for Null	1.000	37266	31546
barriers_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_self_cw	1.71	1.47	[0.27, 11.16]	0.735	[0.83, 1.20]	0.137	0.188	Moderate Evidence for Null	1.000	27968	29099
barriers_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_self_cw	0.67	0.28	[0.26, 1.47]	0.845	[0.83, 1.20]	0.223	0.202	Moderate Evidence for Null	1.000	29742	27471
barriers_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_self_cw	1.37	0.70	[0.46, 4.36]	0.734	[0.83, 1.20]	0.228	0.182	Moderate Evidence for Null	1.000	30625	27331
barriers_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_self_cw	0.86	1.18	[0.05, 17.62]	0.546	[0.83, 1.20]	0.107	0.193	Moderate Evidence for Null	1.000	30311	28639
barriers_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_self_cw	1.76	0.97	[0.59, 5.63]	0.847	[0.83, 1.20]	0.154	0.211	Moderate Evidence for Null	1.000	40322	30462
barriers_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	2.49	0.57	[1.54, 3.87]	NA	NA	NA	NA	NA	1.001	13566	23976
sd(persuasion_self_cw)	0.68	0.31	[0.08, 1.39]	NA	NA	NA	NA	NA	1.001	6918	8734
sd(persuasion_partner_cw)	0.76	0.44	[0.07, 1.83]	NA	NA	NA	NA	NA	1.000	10555	14198
sd(pressure_self_cw)	2.41	0.92	[0.87, 4.59]	NA	NA	NA	NA	NA	1.000	15602	15620
sd(pressure_partner_cw)	1.84	1.20	[0.13, 4.57]	NA	NA	NA	NA	NA	1.000	14595	15886
sd(pushing_self_cw)	0.62	0.36	[0.05, 1.47]	NA	NA	NA	NA	NA	1.000	9022	12138
sd(pushing_partner_cw)	0.51	0.43	[0.02, 1.72]	NA	NA	NA	NA	NA	1.000	13904	21185
sd(barriers_self_cw)	1.52	0.67	[0.24, 2.97]	NA	NA	NA	NA	NA	1.000	11717	10547
sd(barriers_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_self_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Additional Parameters
sigma	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

plot(
  bayestestR::p_direction(is_reactance),
  priors = TRUE
) + 
  coord_cartesian(xlim = c(-6, 6)) +
  theme_bw()

## Warning in `==.default`(dens$Parameter, parameter): longer object length
## is not a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple
## of shorter object length

plot(
  bayestestR::rope(is_reactance, ci = 1)
) + theme_bw()

## Possible multicollinearity between b_persuasion_partner_cb and
##   b_persuasion_self_cb (r = 0.72). This might lead to inappropriate
##   results. See 'Details' in '?rope'.

conditional_spaghetti(
  is_reactance, 
  effects = c('pressure_self_cw', 'pushing_self_cw'),
  group_var = 'coupleID',
  plot_full_range = TRUE
)

\(pressure_self_cw <img src="07_SensitivityBarriersFacilitatorsInteract_files/figure-html/report_is_reactance-3.png" width="2400" />\)pushing_self_cw

hypothesis(is_reactance, "exp(pressure_self_cw) > exp(pushing_self_cw)")

## Hypothesis Tests for class b:
##                 Hypothesis Estimate Est.Error CI.Lower CI.Upper
## 1 (exp(pressure_sel... > 0     5.59     13.16    -0.74    20.62
##   Evid.Ratio Post.Prob Star
## 1       6.48      0.87     
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.

Report All Models

summary_all_models <- report_side_by_side(
  pa_sub,
  pa_obj_log,
  mood_gauss,
  is_reactance,
  
  stats_to_report = c('CI'),
  model_rows_random = model_rows_random,
  model_rows_fixed = model_rows_fixed,
  model_rownames_random = model_rownames_random,
  model_rownames_fixed = model_rownames_fixed
)

[1] “pa_sub”

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Coefficients were exponentiated.
## Double check if this was intended.

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Some rows from the model were
## omitted due to your provided model_rows_fixed or model_rows_random
## vectors. Missing rows: sd(persuasion_partner_cw:barriers_self_cw),
## sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw)

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Length of model_rownames does not
## match number of rows

[1] “pa_obj_log”

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Coefficients were exponentiated.
## Double check if this was intended.

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Some rows from the model were
## omitted due to your provided model_rows_fixed or model_rows_random
## vectors. Missing rows: sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw),
## sd(persuasion_partner_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw)

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Length of model_rownames does not
## match number of rows

[1] “mood_gauss”

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Some rows from the model were
## omitted due to your provided model_rows_fixed or model_rows_random
## vectors. Missing rows: sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw),
## sd(persuasion_partner_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw)
## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Length of model_rownames does not
## match number of rows

[1] “is_reactance”

## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Some rows from the model were
## omitted due to your provided model_rows_fixed or model_rows_random
## vectors. Missing rows: sd(persuasion_self_cw:barriers_self_cw),
## sd(pressure_self_cw:barriers_self_cw),
## sd(pushing_self_cw:barriers_self_cw),
## sd(persuasion_partner_cw:barriers_self_cw),
## sd(pressure_partner_cw:barriers_self_cw),
## sd(pushing_partner_cw:barriers_self_cw)
## Warning in summarize_brms(model, exponentiate = exponentiate,
## stats_to_report = stats_to_report, : Length of model_rownames does not
## match number of rows

summary_all_models <- summary_all_models %>%
  print_df(
    rows_to_pack = rows_to_pack
    ) %>%
  add_header_above(
    c(
      " ", "Hurdle Component" = 2, "Non-Zero Component" = 2,
      " " = 6
    )
  ) %>%
  add_header_above(
    c(" ", "Subjective MVPA Hurdle Lognormal" = 4,  
      "Device-Based MVPA Log (Gaussian)" = 2, 
      "Mood Gaussian" = 2,
      #"Reactance Ordinal" = 2,
      "Reactance Dichotome" = 2
    )
  )

export_xlsx(
  summary_all_models, 
  rows_to_pack = rows_to_pack,
  file.path("Output", paste0("AllModels", suffix, ".xlsx")), 
  merge_option = 'both', 
  simplify_2nd_row = FALSE,
  line_above_rows = c(1,2),
  line_below_rows = c(-1)
)

  
summary_all_models

	Subjective MVPA Hurdle Lognormal				Device-Based MVPA Log (Gaussian)		Mood Gaussian		Reactance Dichotome
	Hurdle Component		Non-Zero Component
	exp(Est.)_hu pa_sub	95% CI_hu pa_sub	exp(Est.)_nonzero pa_sub	95% CI_nonzero pa_sub	exp(Est.) pa_obj_log	95% CI pa_obj_log	Est. mood_gauss	95% CI mood_gauss	OR is_reactance	95% CI is_reactance
Intercept	2.89***	[ 1.62, 5.14]	49.15***	[41.78, 58.06]	122.92***	[108.77, 138.83]	3.80***	[ 3.59, 4.02]	0.47	[0.16, 1.34]
Within-Person Effects
persuasion_self_cw	1.61***	[ 1.30, 2.07]	1.04	[ 0.99, 1.10]	1.03	[ 0.99, 1.06]	0.01	[-0.03, 0.06]	0.63*	[0.38, 0.95]
persuasion_partner_cw	1.44***	[ 1.17, 1.86]	1.02	[ 0.98, 1.07]	1.03	[ 0.99, 1.07]	0.01	[-0.04, 0.06]	1.07	[0.60, 2.14]
pressure_self_cw	0.84	[ 0.43, 1.92]	0.90	[ 0.78, 1.03]	0.95	[ 0.85, 1.05]	-0.02	[-0.16, 0.11]	4.29*	[1.00, 32.57]
pressure_partner_cw	1.64	[ 0.85, 5.26]	0.96	[ 0.86, 1.06]	0.98	[ 0.90, 1.06]	-0.04	[-0.17, 0.08]	1.64	[0.26, 16.69]
pushing_self_cw	1.22	[ 0.82, 1.84]	0.98	[ 0.92, 1.05]	1.03	[ 0.97, 1.08]	0.02	[-0.06, 0.10]	1.70*	[1.10, 3.13]
pushing_partner_cw	1.60*	[ 1.07, 2.78]	0.96	[ 0.90, 1.02]	0.99	[ 0.94, 1.04]	0.08	[ 0.00, 0.15]	0.83	[0.40, 1.85]
day	0.88	[ 0.54, 1.42]	1.03	[ 0.89, 1.18]	0.97	[ 0.88, 1.07]	0.25**	[ 0.10, 0.39]	1.42	[0.34, 5.70]
plan_selfPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
plan_partnerPlan	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_self_cw	0.68	[ 0.42, 1.12]	1.08	[ 0.97, 1.20]	0.97	[ 0.91, 1.03]	-0.26***	[-0.38, -0.12]	0.82	[0.25, 2.82]
barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cw	NA	NA	NA	NA	1.00***	[ 1.00, 1.00]	NA	NA	NA	NA
Between-Person Effects
persuasion_self_cb	0.91	[ 0.25, 3.09]	1.07	[ 0.76, 1.50]	0.96	[ 0.69, 1.33]	0.07	[-0.52, 0.63]	4.16	[0.25, 90.18]
persuasion_partner_cb	1.35	[ 0.38, 4.59]	0.94	[ 0.67, 1.31]	0.82	[ 0.59, 1.15]	0.33	[-0.25, 0.89]	7.65	[0.42, 218.29]
pressure_self_cb	0.23	[ 0.04, 1.24]	0.69	[ 0.31, 1.59]	1.16	[ 0.81, 1.67]	-0.02	[-0.59, 0.56]	68.56	[0.81, 13217.49]
pressure_partner_cb	0.20	[ 0.03, 1.03]	0.51	[ 0.23, 1.17]	1.06	[ 0.75, 1.51]	-0.27	[-0.83, 0.31]	0.65	[0.00, 111.32]
pushing_self_cb	1.35	[ 0.23, 7.96]	1.50	[ 0.83, 2.70]	1.14	[ 0.70, 1.82]	0.22	[-0.61, 1.04]	0.81	[0.01, 59.15]
pushing_partner_cb	1.76	[ 0.30, 10.33]	1.65	[ 0.91, 2.95]	1.29	[ 0.81, 2.06]	0.01	[-0.82, 0.82]	0.01*	[0.00, 0.70]
barriers_self_cb	0.50**	[ 0.30, 0.83]	0.92	[ 0.78, 1.09]	0.78***	[ 0.70, 0.87]	-0.57***	[-0.72, -0.41]	0.67	[0.07, 5.80]
barriers_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cb	1.20	[ 0.82, 1.77]	1.14*	[ 1.03, 1.27]	0.97	[ 0.89, 1.05]	0.32***	[ 0.21, 0.44]	0.99	[0.26, 3.96]
facilitators_partner_cb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
weartime_self_cb	NA	NA	NA	NA	1.00*	[ 1.00, 1.00]	NA	NA	NA	NA
Interactions
persuasion_self_cw:barriers_self_cw	0.95	[ 0.62, 1.46]	0.99	[ 0.92, 1.07]	0.96	[ 0.92, 1.01]	-0.01	[-0.09, 0.06]	0.93	[0.51, 1.65]
barriers_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_self_cw	0.49	[ 0.15, 1.54]	1.02	[ 0.79, 1.31]	1.05	[ 0.89, 1.26]	-0.15	[-0.51, 0.20]	1.71	[0.27, 11.16]
barriers_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_self_cw	1.94	[ 0.99, 4.10]	0.96	[ 0.86, 1.07]	0.98	[ 0.91, 1.05]	0.07	[-0.03, 0.17]	0.67	[0.26, 1.47]
barriers_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_self_cw	0.68*	[ 0.46, 0.95]	1.06	[ 0.99, 1.13]	1.03	[ 0.98, 1.09]	0.02	[-0.05, 0.09]	1.37	[0.46, 4.36]
barriers_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_self_cw	0.82	[ 0.26, 2.53]	0.90	[ 0.75, 1.05]	0.97	[ 0.85, 1.10]	-0.06	[-0.27, 0.13]	0.86	[0.05, 17.62]
barriers_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_self_cw	1.71	[ 0.99, 3.43]	1.05	[ 0.95, 1.18]	1.07	[ 0.99, 1.16]	0.09	[-0.02, 0.22]	1.76	[0.59, 5.63]
barriers_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_self_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_self_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_self_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:barriers_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
barriers_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
persuasion_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:persuasion_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pressure_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pressure_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
pushing_partner_cw:facilitators_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
facilitators_partner_cw:pushing_partner_cw	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Random Effects
sd(Intercept)	1.44	[1.08, 1.97]	0.31	[0.23, 0.42]	0.33	[0.25, 0.43]	0.61	[0.48, 0.79]	2.49	[1.54, 3.87]
sd(persuasion_self_cw)	0.17	[0.01, 0.51]	0.10	[0.05, 0.16]	0.06	[0.02, 0.10]	0.04	[0.00, 0.11]	0.68	[0.08, 1.39]
sd(persuasion_partner_cw)	0.20	[0.01, 0.54]	0.05	[0.01, 0.11]	0.05	[0.01, 0.10]	0.05	[0.00, 0.12]	0.76	[0.07, 1.83]
sd(pressure_self_cw)	0.54	[0.03, 2.05]	0.08	[0.00, 0.30]	0.07	[0.00, 0.24]	0.07	[0.00, 0.26]	2.41	[0.87, 4.59]
sd(pressure_partner_cw)	0.53	[0.03, 2.08]	0.05	[0.00, 0.20]	0.04	[0.00, 0.13]	0.08	[0.00, 0.28]	1.84	[0.13, 4.57]
sd(pushing_self_cw)	0.61	[0.07, 1.30]	0.07	[0.00, 0.16]	0.08	[0.01, 0.15]	0.09	[0.01, 0.20]	0.62	[0.05, 1.47]
sd(pushing_partner_cw)	0.64	[0.07, 1.47]	0.06	[0.00, 0.15]	0.05	[0.00, 0.11]	0.09	[0.01, 0.21]	0.51	[0.02, 1.72]
sd(barriers_self_cw)	1.27	[0.91, 1.79]	0.20	[0.08, 0.33]	0.14	[0.08, 0.21]	0.33	[0.24, 0.45]	1.52	[0.24, 2.97]
sd(barriers_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_self_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
sd(facilitators_partner_cw)	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Additional Parameters
sigma	NA	NA	0.66	[0.63, 0.69]	0.53	[0.51, 0.55]	0.84	[0.81, 0.87]	NA	NA

Plot Interaction effect.

1. Probability of being active (crossing the hurdle)

library(ggplot2)
library(plotly)


p <- conditional_effects(
  pa_sub, 
  dpar = 'hu',
  effects = 'pushing_self_cw:barriers_self_cw',
  re_formula = NA, 
  method = 'posterior_linpred',
  plot = FALSE
)

p2 <- p

p2[[1]]$estimate__ <- 1 - p[[1]]$estimate__
p2[[1]]$lower__ <- 1 - p[[1]]$upper__
p2[[1]]$upper__ <- 1 - p[[1]]$lower__

plots <- plot(p2, theme = theme_minimal(), plot = FALSE) 

plots[[1]] + 
  scale_color_manual(values = c("steelblue", "forestgreen", "coral")) +
  scale_fill_manual(values = c("steelblue", "forestgreen", "coral"))

Test slope of pushing at high levels of barriers

library(tidybayes)
library(dplyr)
library(ggplot2)

plot_slope_by_moderator <- function(
  mod,            
  df,             
  moderator,      
  predictor_par,  
  interaction_par,
  exponentiate = FALSE,   
  n_points = 1000,
  fill_colors = c("Significant" = "steelblue",
                  "Non-significant" = "coral"),
  line_colors = fill_colors,  # Reuse fill colors by default
  vjust_val = -1.5
) {
  
  # Generate a sequence of moderator values
  moderator_seq <- seq(
    from = min(df[[moderator]], na.rm = TRUE),
    to = max(df[[moderator]], na.rm = TRUE),
    length.out = n_points
  )
  
  # Extract posterior draws for the parameters of interest
  library(rlang)
  predictor_sym <- rlang::sym(predictor_par)
  interaction_sym <- rlang::sym(interaction_par)

  post_draws <- mod %>%
    spread_draws(!!predictor_sym, !!interaction_sym)
  
  # For each moderator value, compute the slope for each posterior draw
  slope_list <- lapply(moderator_seq, function(mod_val) {
    tibble(
      moderator_value = mod_val,
      slope = - (post_draws[[predictor_par]] + mod_val * post_draws[[interaction_par]])
    )
  })
  
  # Combine the list into a single data frame
  slope_df <- bind_rows(slope_list)
  
  # Summarize the posterior for each moderator value (using a 95% credible interval)
  slope_summary <- slope_df %>%
    group_by(moderator_value) %>%
    summarize(
      slope_median = median(slope),
      slope_lower  = quantile(slope, probs = 0.025),
      slope_upper  = quantile(slope, probs = 0.975)
    ) %>%
    # Create significance flag: "sig" if CI excludes 0, "ns" otherwise
    mutate(sig_flag = if_else(slope_lower > 0 | slope_upper < 0, "sig", "ns")) %>%
    arrange(moderator_value)
  
  # If exponentiate == TRUE, transform slope columns
  if (exponentiate) {
    slope_summary <- slope_summary %>%
      mutate(
        slope_median_exp = exp(slope_median),
        slope_lower_exp  = exp(slope_lower),
        slope_upper_exp  = exp(slope_upper)
      )
  }
  
  # Identify boundary rows and duplicate them for each category
  slope_summary <- slope_summary %>%
    mutate(
      next_sig_flag = lead(sig_flag),
      is_boundary   = sig_flag != next_sig_flag & !is.na(next_sig_flag)
    )
  
  boundary_rows <- slope_summary %>%
    filter(is_boundary)
  
  boundary_rows_next <- boundary_rows
  boundary_rows_next$sig_flag <- boundary_rows_next$next_sig_flag
  
  slope_summary_extended <- bind_rows(slope_summary, boundary_rows_next)
  
  # Split into "sig" and "ns"
  slope_summary_sig <- slope_summary_extended %>% filter(sig_flag == "sig")
  slope_summary_ns  <- slope_summary_extended %>% filter(sig_flag == "ns")
  
  # Build the base plot:
  #    We'll use either slope_{median, lower, upper} or slope_{median_exp, lower_exp, upper_exp} 
  #    depending on exponentiate.
  if (exponentiate) {
    median_col <- "slope_median_exp"
    lower_col <- "slope_lower_exp"
    upper_col <- "slope_upper_exp"
    y_lab <- "Odds Ratio (Exponentiated Slope)"
    ref_line <- 1  # reference line for ORs is 1
  } else {
    median_col <- "slope_median"
    lower_col <- "slope_lower"
    upper_col <- "slope_upper"
    y_lab <- "Slope (Log-Odds Scale)"
    ref_line <- 0
  }
  
  p <- ggplot() +
    # Non-significant region
    geom_ribbon(
      data = slope_summary_ns,
      aes_string(
        x = "moderator_value",
        ymin = lower_col,
        ymax = upper_col,
        fill = '"Non-significant"'
      ),
      alpha = 0.2
    ) +
    geom_line(
      data = slope_summary_ns,
      aes_string(
        x = "moderator_value",
        y = median_col,
        color = '"Non-significant"'
      ),
      size = 1
    ) +
    # Significant region
    geom_ribbon(
      data = slope_summary_sig,
      aes_string(
        x = "moderator_value",
        ymin = lower_col,
        ymax = upper_col,
        fill = '"Significant"'
      ),
      alpha = 0.2
    ) +
    geom_line(
      data = slope_summary_sig,
      aes_string(
        x = "moderator_value",
        y = median_col,
        color = '"Significant"'
      ),
      size = 1
    ) +
    # Reference line (0 for log-odds, 1 for odds-ratios)
    geom_hline(
      yintercept = ref_line, 
      linetype = "dashed"
    ) +
    # Manual scales for fill & color
    scale_fill_manual(
      values = fill_colors
    ) +
    scale_color_manual(
      values = line_colors
    ) +
    labs(
      x = moderator,
      y = y_lab,
      fill = "Significance",
      color = "Significance"
    ) +
    theme_minimal()
  
  # Add boundary lines (where significance changes) & labels
  p2 <- p +
    geom_vline(
      data = boundary_rows,
      aes(xintercept = moderator_value),
      color = fill_colors["Significant"],
      alpha = 0.35,
      size = 0.7,
      linetype = "dashed"
    ) +
    geom_text(
      data = boundary_rows,
      aes(x = moderator_value, y = -Inf, label = round(moderator_value, 3)),
      vjust = vjust_val,
      size  = 3.7
    )
  
  return(p2)
}


p_log_odds <- plot_slope_by_moderator(
  mod = pa_sub, 
  df = df_double, 
  moderator = "barriers_self_cw", 
  predictor_par = "b_hu_pushing_self_cw", 
  interaction_par = "b_hu_pushing_self_cw:barriers_self_cw",
  exponentiate = FALSE
)

p_log_odds

p_odds_ratio <- plot_slope_by_moderator(
  mod = pa_sub, 
  df = df_double, 
  moderator = "barriers_self_cw", 
  predictor_par = "b_hu_pushing_self_cw", 
  interaction_par = "b_hu_pushing_self_cw:barriers_self_cw",
  exponentiate = TRUE,
  vjust_val = -12,
) + coord_cartesian(ylim = c(1, 18))

p_odds_ratio

# Export
ggsave(
  filename = file.path("Output", "InteractPlot_log_odds.png"), 
  plot = p_log_odds,
  width = 8,
  height = 5,
  dpi = 300,
  bg = 'white'
)

ggsave(
  filename = file.path("Output", "InteractPlot_odds_ratio.png"), 
  plot = p_odds_ratio, 
  width = 8, 
  height = 5, 
  dpi = 300,
  bg = 'white'
)

2. Expected MVPA when the hurdle was crossed (interaction NOT significant)

p3 <- conditional_effects(
  pa_sub, 
  dpar = 'mu',
  effects = 'pushing_self_cw:barriers_self_cw',
  #re_formula = NULL, 
  plot = FALSE
)

p3[[1]]$estimate__ <- exp(p3[[1]]$estimate__)
p3[[1]]$lower__ <- exp(p3[[1]]$lower__)
p3[[1]]$upper__ <- exp(p3[[1]]$upper__)

plot(p3, ask = FALSE)

2. Expected physical activity in Minutes (when both components combined)

# Expected physical activity
p4 <- conditional_effects(
  pa_sub, 
  effects = 'pushing_self_cw:barriers_self_cw',
  #re_formula = NULL,
  plot = FALSE
)

plot(p4, ask = FALSE)

report::report_system()

Analyses were conducted using the R Statistical language (version 4.4.2; R Core Team, 2024) on Windows 11 x64 (build 26100)

report::report_packages()

• rmarkdown (version 2.29; Allaire J et al., 2024)
• beepr (version 2.0; Bååth R, 2024)
• R.methodsS3 (version 1.8.2; Bengtsson H, 2003)
• R.oo (version 1.27.0; Bengtsson H, 2003)
• R.utils (version 2.12.3; Bengtsson H, 2023)
• brms (version 2.22.0; Bürkner P, 2017)
• posterior (version 1.6.0; Bürkner P et al., 2024)
• extrafont (version 0.19; Chang W, 2023)
• digest (version 0.6.37; Eddelbuettel D, 2024)
• Rcpp (version 1.0.13.1; Eddelbuettel D et al., 2024)
• bayesplot (version 1.11.1; Gabry J, Mahr T, 2024)
• lubridate (version 1.9.3; Grolemund G, Wickham H, 2011)
• DHARMa (version 0.4.7; Hartig F, 2024)
• rlang (version 1.1.4; Henry L, Wickham H, 2024)
• tidybayes (version 3.0.7; Kay M, 2024)
• wbCorr (version 0.1.22; Küng P, 2023)
• see (version 0.9.0; Lüdecke D et al., 2021)
• tibble (version 3.2.1; Müller K, Wickham H, 2023)
• patchwork (version 1.3.0; Pedersen T, 2024)
• R (version 4.4.2; R Core Team, 2024)
• openxlsx (version 4.2.7.1; Schauberger P, Walker A, 2024)
• plotly (version 4.10.4; Sievert C, 2020)
• ggplot2 (version 3.5.1; Wickham H, 2016)
• forcats (version 1.0.0; Wickham H, 2023)
• stringr (version 1.5.1; Wickham H, 2023)
• rvest (version 1.0.4; Wickham H, 2024)
• tidyverse (version 2.0.0; Wickham H et al., 2019)
• readxl (version 1.4.3; Wickham H, Bryan J, 2023)
• dplyr (version 1.1.4; Wickham H et al., 2023)
• purrr (version 1.0.2; Wickham H, Henry L, 2023)
• readr (version 2.1.5; Wickham H et al., 2024)
• xml2 (version 1.3.6; Wickham H et al., 2023)
• tidyr (version 1.3.1; Wickham H et al., 2024)
• ggridges (version 0.5.6; Wilke C, 2024)
• knitr (version 1.49; Xie Y, 2024)
• kableExtra (version 1.4.0; Zhu H, 2024)

report::cite_packages()

• Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2024). rmarkdown: Dynamic Documents for R. R package version 2.29, https://github.com/rstudio/rmarkdown. Xie Y, Allaire J, Grolemund G (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9781138359338, https://bookdown.org/yihui/rmarkdown. Xie Y, Dervieux C, Riederer E (2020). R Markdown Cookbook. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9780367563837, https://bookdown.org/yihui/rmarkdown-cookbook.
• Bååth R (2024). beepr: Easily Play Notification Sounds on any Platform. R package version 2.0, https://github.com/rasmusab/beepr.
• Bengtsson H (2003). “The R.oo package - Object-Oriented Programming with References Using Standard R Code.” In Hornik K, Leisch F, Zeileis A (eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). https://www.r-project.org/conferences/DSC-2003/Proceedings/Bengtsson.pdf.
• Bengtsson H (2003). “The R.oo package - Object-Oriented Programming with References Using Standard R Code.” In Hornik K, Leisch F, Zeileis A (eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). https://www.r-project.org/conferences/DSC-2003/Proceedings/Bengtsson.pdf.
• Bengtsson H (2023). R.utils: Various Programming Utilities. R package version 2.12.3, https://github.com/HenrikBengtsson/R.utils, https://henrikbengtsson.github.io/R.utils/.
• Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software, 80(1), 1-28. doi:10.18637/jss.v080.i01 https://doi.org/10.18637/jss.v080.i01. Bürkner P (2018). “Advanced Bayesian Multilevel Modeling with the R Package brms.” The R Journal, 10(1), 395-411. doi:10.32614/RJ-2018-017 https://doi.org/10.32614/RJ-2018-017. Bürkner P (2021). “Bayesian Item Response Modeling in R with brms and Stan.” Journal of Statistical Software, 100(5), 1-54. doi:10.18637/jss.v100.i05 https://doi.org/10.18637/jss.v100.i05.
• Bürkner P, Gabry J, Kay M, Vehtari A (2024). “posterior: Tools for Working with Posterior Distributions.” R package version 1.6.0, https://mc-stan.org/posterior/. Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P (2021). “Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with discussion).” Bayesian Analysis.
• Chang W (2023). extrafont: Tools for Using Fonts. R package version 0.19, https://github.com/wch/extrafont.
• Eddelbuettel D (2024). digest: Create Compact Hash Digests of R Objects. R package version 0.6.37, https://dirk.eddelbuettel.com/code/digest.html, https://github.com/eddelbuettel/digest.
• Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I, Bates D, Chambers J (2024). Rcpp: Seamless R and C++ Integration. R package version 1.0.13-1, https://dirk.eddelbuettel.com/code/rcpp.html, https://github.com/RcppCore/Rcpp, https://www.rcpp.org. Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software, 40(8), 1-18. doi:10.18637/jss.v040.i08 https://doi.org/10.18637/jss.v040.i08. Eddelbuettel D (2013). Seamless R and C++ Integration with Rcpp. Springer, New York. doi:10.1007/978-1-4614-6868-4 https://doi.org/10.1007/978-1-4614-6868-4, ISBN 978-1-4614-6867-7. Eddelbuettel D, Balamuta J (2018). “Extending R with C++: A Brief Introduction to Rcpp.” The American Statistician, 72(1), 28-36. doi:10.1080/00031305.2017.1375990 https://doi.org/10.1080/00031305.2017.1375990.
• Gabry J, Mahr T (2024). “bayesplot: Plotting for Bayesian Models.” R package version 1.11.1, https://mc-stan.org/bayesplot/. Gabry J, Simpson D, Vehtari A, Betancourt M, Gelman A (2019). “Visualization in Bayesian workflow.” J. R. Stat. Soc. A, 182, 389-402. doi:10.1111/rssa.12378 https://doi.org/10.1111/rssa.12378.
• Grolemund G, Wickham H (2011). “Dates and Times Made Easy with lubridate.” Journal of Statistical Software, 40(3), 1-25. https://www.jstatsoft.org/v40/i03/.
• Hartig F (2024). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.7, http://florianhartig.github.io/DHARMa/.
• Henry L, Wickham H (2024). rlang: Functions for Base Types and Core R and ‘Tidyverse’ Features. R package version 1.1.4, https://github.com/r-lib/rlang, https://rlang.r-lib.org.
• Kay M (2024). tidybayes: Tidy Data and Geoms for Bayesian Models. doi:10.5281/zenodo.1308151 https://doi.org/10.5281/zenodo.1308151, R package version 3.0.7, http://mjskay.github.io/tidybayes/.
• Küng P (2023). wbCorr: Bivariate Within- and Between-Cluster Correlations. University of Zürich. R package version 0.1.22. https://github.com/Pascal-Kueng/wbCorr.
• Lüdecke D, Patil I, Ben-Shachar M, Wiernik B, Waggoner P, Makowski D (2021). “see: An R Package for Visualizing Statistical Models.” Journal of Open Source Software, 6(64), 3393. doi:10.21105/joss.03393 https://doi.org/10.21105/joss.03393.
• Müller K, Wickham H (2023). tibble: Simple Data Frames. R package version 3.2.1, https://github.com/tidyverse/tibble, https://tibble.tidyverse.org/.
• Pedersen T (2024). patchwork: The Composer of Plots. R package version 1.3.0, https://github.com/thomasp85/patchwork, https://patchwork.data-imaginist.com.
• R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
• Schauberger P, Walker A (2024). openxlsx: Read, Write and Edit xlsx Files. R package version 4.2.7.1, https://github.com/ycphs/openxlsx, https://ycphs.github.io/openxlsx/index.html.
• Sievert C (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC. ISBN 9781138331457, https://plotly-r.com.
• Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
• Wickham H (2023). forcats: Tools for Working with Categorical Variables (Factors). R package version 1.0.0, https://github.com/tidyverse/forcats, https://forcats.tidyverse.org/.
• Wickham H (2023). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.5.1, https://github.com/tidyverse/stringr, https://stringr.tidyverse.org.
• Wickham H (2024). rvest: Easily Harvest (Scrape) Web Pages. R package version 1.0.4, https://github.com/tidyverse/rvest, https://rvest.tidyverse.org/.
• Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
• Wickham H, Bryan J (2023). readxl: Read Excel Files. R package version 1.4.3, https://github.com/tidyverse/readxl, https://readxl.tidyverse.org.
• Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://github.com/tidyverse/dplyr, https://dplyr.tidyverse.org.
• Wickham H, Henry L (2023). purrr: Functional Programming Tools. R package version 1.0.2, https://github.com/tidyverse/purrr, https://purrr.tidyverse.org/.
• Wickham H, Hester J, Bryan J (2024). readr: Read Rectangular Text Data. R package version 2.1.5, https://github.com/tidyverse/readr, https://readr.tidyverse.org.
• Wickham H, Hester J, Ooms J (2023). xml2: Parse XML. R package version 1.3.6, https://github.com/r-lib/xml2, https://xml2.r-lib.org/.
• Wickham H, Vaughan D, Girlich M (2024). tidyr: Tidy Messy Data. R package version 1.3.1, https://github.com/tidyverse/tidyr, https://tidyr.tidyverse.org.
• Wilke C (2024). ggridges: Ridgeline Plots in ‘ggplot2’. R package version 0.5.6, https://wilkelab.org/ggridges/.
• Xie Y (2024). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.49, https://yihui.org/knitr/. Xie Y (2015). Dynamic Documents with R and knitr, 2nd edition. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 978-1498716963, https://yihui.org/knitr/. Xie Y (2014). “knitr: A Comprehensive Tool for Reproducible Research in R.” In Stodden V, Leisch F, Peng RD (eds.), Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595.
• Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.4.0, https://github.com/haozhu233/kableExtra, http://haozhu233.github.io/kableExtra/.